{"cells":[{"metadata":{},"cell_type":"markdown","source":"<h1><center>BirdSound Recognition</center></h1>\n\n![](https://m.media-amazon.com/images/I/81g3oOHeYZL._SS500_.jpg)"},{"metadata":{},"cell_type":"markdown","source":"# Introduction\n\nOur challenge in this competition is to identify which birds are calling in long recordings, given training data generated in meaningfully different contexts. This is the exact problem facing scientists trying to automate the remote monitoring of bird populations.\n\n\n### Files\n\n1. `train_audio` -  The train data consists of short recordings of individual bird calls\n\n2. `test_audio`  -  The hidden test_audio directory contains approximately 150 recordings in mp3 format, each roughly 10 minutes long. \nThe recordings were taken at three separate remote locations in North America. `Sites 1` and `2` were labeled in `5 second increments` and need matching predictions, but due to the time consuming nature of the labeling process the `site 3` files are only labeled at the file level.\n\n3. `test.csv` Only the first three rows are available for download; the full test.csv is in the hidden test set.\n\n4.`train.csv` - A wide range of metadata is provided for the training data. The most directly relevant fields are:\n\n* `ebird_code`: a code for the bird species. You can review detailed information about the bird codes by appending the code to https://ebird.org/species/, such as https://ebird.org/species/amecro for the American Crow.\n    \n* `recodist`: the user who provided the recording.\n\n* `location`: where the recording was taken. Some bird species may have local call 'dialects', so you may want to seek geographic diversity in your training data.\n\n* `date`: while some bird calls can be made year round, such as an alarm call, some are restricted to a specific season. You may want to seek temporal diversity in your training data.\n\n* `filename`: the name of the associated audio file."},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install nlpaug","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Getting our tools ready"},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"# tool box\n\nimport numpy as np\nimport pandas as pd\n\nimport geopandas as gpd\nfrom shapely.geometry import Point, Polygon\n\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport plotly.express as px\nimport matplotlib.gridspec as gridspec\nfrom mpl_toolkits.basemap import Basemap\nimport plotly.express as px\n\nimport IPython.display as ipd  # To play sound in the notebook\nimport librosa\nimport librosa.display\nimport sklearn\nimport librosa.display as librosa_display\nimport nlpaug\nimport nlpaug.augmenter.audio as naa\n\nimport os\nfrom PIL import Image\nimport pathlib\nimport csv\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder, StandardScaler,MinMaxScaler\nimport keras\nfrom keras import layers\nimport random\nfrom keras.models import Sequential\nfrom tqdm import tqdm\nfrom keras.layers import Dense\nfrom keras.layers import Dropout\nfrom keras.layers import LSTM\nfrom keras.utils import np_utils, to_categorical\nfrom keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, EarlyStopping\n\nimport warnings\nwarnings.filterwarnings('ignore')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# General Settings\n\n# display all the columns in the dataset\npd.pandas.set_option('display.max_columns', None)\n\n# Setting color palette.\npurple_black = [\n\"#9b59b6\", \"#3498db\", \"#95a5a6\", \"#e74c3c\", \"#34495e\", \"#2ecc71\"\n]\n\n# Setting plot styling.\n#plt.style.use('ggplot')\nplt.style.use('fivethirtyeight')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"base_path='../input/birdsong-recognition/'\naudio_path=base_path+'train_audio/'","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Let's check the metadata (.csv files)"},{"metadata":{"trusted":true},"cell_type":"code","source":"# training dataset\ntrain = pd.read_csv(\"/kaggle/input/birdsong-recognition/train.csv\")\ntrain.shape","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"📌 <b>Key Observations:</b>\n* Training dataset `train.csv` has 21375 rows and 35 columns"},{"metadata":{"trusted":true},"cell_type":"code","source":"# lets inspect first few rows of the dataset\ntrain.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"📌 <b>Key Observations:</b>\n\n* Intersting point to note here is that, each recording has multiple labels associated with it, `species`, `primary label` and `secondary label`. This means that recordings may contain voice of more than one bird, which is quite natural because birds normally sing in groups!"},{"metadata":{"trusted":true},"cell_type":"code","source":"# check null values\ntrain.isnull().sum().sort_values(ascending = False)[train.isnull().sum()!=0]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# visualize missing values:\nplt.figure(constrained_layout=True, figsize=(12, 8))\npercent = (train.isnull().sum().sort_values(ascending=False) / len(train) *\n           100)[(train.isnull().sum().sort_values(ascending=False) / len(train) *\n                 100) != 0]\n\nmissing = pd.DataFrame({\"missing%\":percent})\n\nsns.barplot(x=missing.index,\n            y='missing%',\n            data=missing,\n            palette=purple_black)\nplt.title('Train Data Missing Values')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Test Dataset"},{"metadata":{"trusted":true},"cell_type":"code","source":"test = pd.read_csv(\"/kaggle/input/birdsong-recognition/test.csv\")\ntest.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"📌 Key Observations:\n    Only the first three rows are available for download; the full test.csv is in the hidden test set. \n   \nTest dataset has following columns:\n\n`site`: Site ID.\n\n`row_id`: ID code for the row.\n\n`seconds`: the second ending the time window, if any. Site 3 time windows cover the entire audio file and have null entries for seconds.\n\n`audio_id`: ID code for the audio file."},{"metadata":{},"cell_type":"markdown","source":"# Exploratory Data Analysis\n\n![image.png](attachment:image.png)","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{},"cell_type":"markdown","source":"### 1. Let's check the class distribution (no. of unique birds in the dataset)"},{"metadata":{"trusted":true},"cell_type":"code","source":"# no of unique classes(birds) in the dataset\nprint(\"dataset has\",train.species.nunique(),\"unique bird's species\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# count wise distribution of bird's species\ncount = train.species.value_counts().sort_values(ascending = False)\ncount","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# lets visualize class distribution in the dataset\nfig = px.pie(count,\n             values=count.values,\n             names=count.index,\n             color_discrete_sequence=purple_black,\n             hole=.4)\nfig.update_traces(textinfo='percent', pull=0.05)\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"📌 Key Observations:\n* We have 264 unique bird species\n* Blackpoll Warbler, American Crow,Veery & Lesser Goldfinch are few of the most frequently present in the dataset "},{"metadata":{},"cell_type":"markdown","source":"### 2. Let's check recording location - Country\n\n* country: Species recorded location"},{"metadata":{"trusted":true},"cell_type":"code","source":"# country\nprint(\"training dataset has data from\",train.country.nunique(),\"unique countries\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# lets visualize top 10 countries\nplt.figure(constrained_layout=True, figsize=(16, 8))\nsns.countplot(train.country,\n              alpha=0.9,              \n              palette=purple_black,\n              order = train.country.value_counts().sort_values(ascending=False).iloc[:10].index,)\nplt.xlabel(\"Country\")\nplt.ylabel(\"Count\")\nplt.title(\"Country wise Distribution\")\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"📌 Key Observations:\n* Majority of the recordings are done in USA, followed by CANADA and MEXICO"},{"metadata":{},"cell_type":"markdown","source":"### 3.Let's explore latitude and longitude, we will plot our birds on the world map\n\n* latitude: latitude co-ordinate of the earth\n* longitude: longitude co-ordinate of the earth"},{"metadata":{"trusted":true},"cell_type":"code","source":"# world shape file\nworld_map = gpd.read_file(\"../input/worldshapefile/world_shapefile.shp\")\n\n# Coordinate reference system\ncrs = {\"init\" : \"epsg:4326\"}","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# let's filter out \"not specified\" values\ndf = train[train[\"latitude\"] != \"Not specified\"]\n\n# convert latitude and longitute to float variables\ndf[\"latitude\"] = df[\"latitude\"].astype(float)\ndf[\"longitude\"] = df[\"longitude\"].astype(float)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# create geometric list\ngeometry = [Point(xy) for xy in zip(df[\"longitude\"], df[\"latitude\"])]\n\n# create geography dataframe\ngeo = gpd.GeoDataFrame(df, crs=crs, geometry=geometry)\n\n# Create ID for species\nspecies = geo[\"species\"].value_counts().reset_index()\nspecies.insert(0, 'ID', range(0, 0 + len(species)))\n\nspecies.columns = [\"ID\", \"species\", \"count\"]\n\n# merge the dataframes\ngeo = pd.merge(geo, species, how=\"left\", on=\"species\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# visualize bird's on the world map!\nfig, ax = plt.subplots(figsize = (20, 9))\nworld_map.plot(ax=ax, alpha=0.4, color=\"blue\")\n\npalette = iter(sns.hls_palette(len(species)))\n\nfor i in range(264):\n    geo[geo[\"ID\"] == i].plot(ax=ax, markersize=30, color=next(palette), marker=\"o\");\n    \nplt.title(\"These colorful small circles are our birds :-)\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### 4. lets explore \"date\" feature"},{"metadata":{"trusted":true},"cell_type":"code","source":"# check the date format\ntrain.date.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# lets pull year from the given date\n\ntrain['year'] = train['date'].apply(lambda x: x.split('-')[0])\n\n# lets visualize year wise distribution\n\nfig = plt.figure(constrained_layout=True, figsize=(20,8))\n\nsns.countplot(train.year,             \n              alpha=0.9,              \n              palette=purple_black,           \n              order = train.year.value_counts().sort_values(ascending=False).iloc[:15].index   \n             )\nplt.xlabel(\"Year\")\nplt.ylabel(\"Count\")\nplt.title('Year-Wise Distribution')\n\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"📌 Key Observations:\n* No. of recording started increasing from 2012, max recordings in 2014"},{"metadata":{"trusted":true},"cell_type":"code","source":"# lets pull month from the date\ntrain['month'] = train['date'].apply(lambda x: x.split('-')[1])\n\n# lets visualize month wise distribution\n\nfig = plt.figure(constrained_layout=True, figsize=(20,8))\n\nsns.countplot(train.month,             \n              alpha=0.9,              \n              palette=purple_black,           \n              order = train.month.value_counts().sort_values(ascending=False).index   \n             )\nplt.xlabel(\"Month\")\nplt.ylabel(\"Count\")\nplt.title('Month-Wise Distribution')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"📌 Key Observations:\n* Max recording are in the month of May(End of Spring) & June(Start of Summer)"},{"metadata":{},"cell_type":"markdown","source":"### 5. Let's check ebird code & sci_name        \n\nebird_code - a code for the bird species. You can review detailed information about the bird codes by appending the code to https://ebird.org/species/, such as https://ebird.org/species/amecro for the American Crow.**\n\n* sci_name: Scientific Name of the Bird         "},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"There are\",train.ebird_code.nunique(),\"ebird codes in the dataset\")\nprint(\"training dataset has\",train.sci_name.nunique(),\"unique sci_names\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# lets visualize ebird code & sci_name\n\nfig = plt.figure(constrained_layout=True, figsize=(20,8))\n\ngrid = gridspec.GridSpec(ncols=4, nrows=1, figure=fig)\n\nax1 = fig.add_subplot(grid[0, :2])\nsns.countplot(train.ebird_code,             \n              alpha=0.9,\n              ax=ax1,\n              palette=purple_black,           \n              order = train.ebird_code.value_counts().sort_values(ascending = False).iloc[:15].index   \n             )\nplt.xlabel(\"Ebird Code\")\nplt.ylabel(\"Count\")\nplt.title('Ebird Code Distribution')\nplt.xticks(rotation=30)\n\nax2 = fig.add_subplot(grid[0, 2:4])\nsns.countplot(train.sci_name,             \n              alpha=0.9,\n              ax=ax2,\n              palette=purple_black,           \n              order = train.sci_name.value_counts().sort_values(ascending = False).iloc[:15].index   \n             )\nplt.xlabel(\"Scientific Name\")\nplt.ylabel(\"Count\")\nplt.title('Scientific Name Distribution')\nplt.xticks(rotation=30)\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"📌 Key Observations:\n\nThere are as many unique ebird codes as the no. of species,also the distribution is same as species, which means each ebird code represnts one particular species\n\nThere are 264 unique scientific names,equal to the no. of species, seems to have 1:1 mapping with species"},{"metadata":{},"cell_type":"markdown","source":"### 6. Lets inspect filename,title,description,xc_id & url\n\n* title : Ebird_Code with Species Name\n* filename: name of the associated audio file present in the train_audio directory.\n* description: Description about the recording provided by the recordist\n* xc_id: xeno-canto bird Id\n* url: xeno-canto Bird Link"},{"metadata":{"trusted":true},"cell_type":"code","source":"# lets check the no. of unique values for the field filename\nprint(\"training dataset has\",train.filename.nunique(),\"unique filenames\")\nprint(\"training dataset has\",train.title.nunique(),\"unique titles\")\nprint(\"training dataset has\",train.description.nunique(),\"unique descriptions\")\nprint(\"training dataset has\",train.xc_id.nunique(),\"unique xc_id\")\nprint(\"training dataset has\",train.url.nunique(),\"unique urls\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"📌 Key Observations:\n\nThere are 21375 unique filenames & titles in the dataset, which is equal to the no. of rows in the dataset, which means each row has a unique filename and a unique title corresponding to a recording\n\nThere are 12694 unique descriptions and 6199 missing values, this field seems to be like a remark field\n\nThere are 21375 unique xc_id & urls in the dataset, which is equal to the no. of rows in the dataset, which means each row has a unique xc_id & unique url corresponding to a recording"},{"metadata":{"trusted":true},"cell_type":"code","source":"# lets check top 3 descriptions and see how it looks like\nfig = plt.figure(constrained_layout=True, figsize=(20, 12))\nsns.countplot(train.description,\n              alpha=0.9,              \n              palette=purple_black,\n              order= train.description.value_counts().sort_values(ascending = False).iloc[:3].index)\n\nplt.xlabel(\"Description\")\nplt.ylabel(\"Count\")\nplt.title('Description Distribution')\n\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### 7. Next set of fields that we will examine are playback_used, channel & rating\n\n* rating: rates the audio quality from 0-5\n* playback_used: Was playback used to lure the bird ?\n* channels: stereo or mono"},{"metadata":{"trusted":true},"cell_type":"code","source":"# lets visualize top Playback used, channel & Ratings fields\n\nfig = plt.figure(constrained_layout=True, figsize=(20, 9))\n\n# Creating a grid:\ngrid = gridspec.GridSpec(ncols=4, nrows=2, figure=fig)\n\n# playback used\nax1 = fig.add_subplot(grid[0, :2])\n\nsns.countplot(train.playback_used,\n              alpha=0.9,\n              ax=ax1,\n              order= train.playback_used.value_counts().sort_values(ascending = False).index,\n              palette=purple_black)\n\nplt.xlabel(\"Playback_Used\")\nplt.ylabel(\"Count\")\nax1.set_title('PlayBack Used Distribution')\n\n# channels.\nax2 = fig.add_subplot(grid[0, 2:])\n\n# Plot the countplot.\nsns.countplot(train.channels,\n              alpha=0.9,\n              ax=ax2,\n              order= train.channels.value_counts().sort_values(ascending = False).index,\n              palette=purple_black)\n\nplt.xlabel(\"Channels\")\nplt.ylabel(\"Count\")\nax2.set_title('Channels Distribution')\n\n# Ratings\nax3 = fig.add_subplot(grid[1, :])\n\nsns.countplot(train.rating,\n              alpha=0.9,\n              ax = ax3,\n              palette=purple_black,              \n              order= train.rating.value_counts().sort_values(ascending = False).index)\n\nplt.xlabel(\"Ratings\")\nplt.ylabel(\"Count\")\nax3.set_title('Ratings Distribution')\n\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"📌 Key Observations:\n\n* For most of the recordings present in the dataset, playback was not used\n* Channels seems to have almost equal distribution\n* Most of the audio have good ratings, kind of an indication that people usually loves bird's voice"},{"metadata":{},"cell_type":"markdown","source":"### 8. Next set of fields that we will examine are pitch, no. of notes & speed\n\n* pitch: Was the Pitch of the Bird Call increasing / decreasing or constant\n* number_of_notes: No: of Syllables\n* speed: whether speed is constant (level), decreasing (decelerating), increasing (accelerating), or both (in either order"},{"metadata":{"trusted":true},"cell_type":"code","source":"# lets visualize pitch, speed & no. of notes\n\nfig = plt.figure(constrained_layout=True, figsize=(20, 9))\n# Creating a grid:\ngrid = gridspec.GridSpec(ncols=4, nrows=2, figure=fig)\n\n# pitch\nax1 = fig.add_subplot(grid[0, :2])\n\nsns.countplot(train.pitch,\n              alpha=0.9,\n              ax=ax1,\n              palette=purple_black,\n              order= train.pitch.value_counts().sort_values(ascending = False).index)\nplt.xlabel(\"Pitch\")\nplt.ylabel(\"Count\")\nax1.set_title('Pitch Distribution')\n\n\n\n# speed\nax2 = fig.add_subplot(grid[0, 2:])\n\n# Plot the countplot.\nsns.countplot(train.speed,\n              alpha=0.9,\n              ax=ax2,\n              palette=purple_black,\n              order= train.speed.value_counts().sort_values(ascending = False).index)\n\nplt.xlabel(\"Speed\")\nplt.ylabel(\"Count\")\nax2.set_title('Speed Distribution')\n\n# number_of_notes\nax3 = fig.add_subplot(grid[1, :])\n\nsns.countplot(train.number_of_notes,\n              alpha=0.9,\n              ax=ax3,\n              palette=purple_black,\n              order= train.number_of_notes.value_counts().sort_values(ascending = False).index)\nplt.xlabel(\"Number Of Notes Distribution\")\nplt.ylabel(\"Count\")\nax3.set_title('Number Of Notes')\n\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"📌 Key Observations:\n\n* pitch/speed - most of the values for these field are not specified\n* number of notes is not specified for majority of the records"},{"metadata":{},"cell_type":"markdown","source":"### 9. Lets take a look at duration\n\n* duration: Total Recording in Seconds"},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.figure(constrained_layout=True, figsize=(12, 8))\nsns.distplot(train.duration,\n            color='coral')\n\nplt.xlabel(\"Duration\")\nplt.ylabel(\"Count\")\nplt.title('Duration Distribution')\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"📌 Key Observations:\n* duration of most of the audio files are between 0 to 300 seconds"},{"metadata":{},"cell_type":"markdown","source":"### 10. Lets inspect primary and secondary labels\n\n* primary_label: Meta-Data for Labeling Birds in Xeno Catalog\n* secondary_labels : Background Birds Identified"},{"metadata":{"trusted":true},"cell_type":"code","source":"# lets check the no. of unique values for the field primary & secondary labels\nprint(\"training dataset has\",train.primary_label.nunique(),\"unique primary labels\")\n\nprint(\"training dataset has\",train.secondary_labels.nunique(),\"unique secondary labels\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# lets visualize primary labels\nplt.figure(constrained_layout=True, figsize=(12, 8))\n\ncount = train.primary_label.value_counts().sort_values(ascending = False)[:50]\n\nfig = px.pie(count,\n             values=count.values,\n             names=count.index,\n             color_discrete_sequence=purple_black,\n             hole=.4)\nfig.update_traces(textinfo='percent', pull=0.05)\n\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# lets visualize seconary labels\nplt.figure(constrained_layout=True, figsize=(12, 8))\n\ncount = train.secondary_labels.value_counts().sort_values(ascending = False)[:20]\n\nfig = px.pie(count,\n             values=count.values,\n             names=count.index,\n             color_discrete_sequence=purple_black,\n             hole=.4)\nfig.update_traces(textinfo='percent', pull=0.05)\n\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"📌 Key Observations:\n* We have 264 unique primary labels, which is equal to the no. of unique species\n* Primary labels are equally distributed in the dataset\n* We have 5385 unique secondary labels, even though for majority of the audio clips, this field is left blank \"[]\" "},{"metadata":{},"cell_type":"markdown","source":"### 11. Lets inspect bird seen, sampling rate and type\n\n* bird_seen: Was the Bird Seen during the recording\n* sampling_rate: Digital Samples recorded per second, most of the samples are 44.1kHz / 48kHz\n* Type of Bird Sound Recorded. Wing Noise / Song / Call / Flight Call"},{"metadata":{"trusted":true},"cell_type":"code","source":"# lets visualize bird_seen, sampling rate and type fields\n\nfig = plt.figure(constrained_layout=True, figsize=(20, 9))\n\n# Creating a grid:\ngrid = gridspec.GridSpec(ncols=4, nrows=2, figure=fig)\n\n# playback used\nax1 = fig.add_subplot(grid[0, :2])\n\nsns.countplot(train.bird_seen,\n              alpha=0.9,\n              ax=ax1,\n              palette=purple_black,              \n              order = train.bird_seen.value_counts().sort_values(ascending = False).index)\nplt.xlabel(\"Bird Seen Distribution\")\nplt.ylabel(\"Count\")\nax1.set_title('Bird Seen')\n\n\n# sampling_rate.\nax2 = fig.add_subplot(grid[0, 2:])\n\n# Plot the countplot.\nsns.countplot(train.sampling_rate,\n              alpha=0.9,\n              ax=ax2,\n              palette=purple_black,\n              order = train.sampling_rate.value_counts().sort_values(ascending = False).index)\n\nplt.xlabel(\"Sampling Rate Distribution\")\nplt.ylabel(\"Count\")\nax2.set_title('Sampling Rate')\n\n# type              \nax3 = fig.add_subplot(grid[1, :])\n\nsns.countplot(train.type              ,\n              alpha=0.9,\n              ax = ax3,\n              palette=purple_black,           \n              order = train.type.value_counts().sort_values(ascending = False).iloc[:10].index)\n            \nplt.xlabel(\"Type Distribution\")\nplt.ylabel(\"Count\")\nplt.xticks(rotation = 30)\nax3.set_title('Type')\n\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"📌 Key Observations:\n\n* In most of the cases birds were seen while recording\n* Sampling rate is mostly 44100 and 48000 Hz\n* In most if the cases birds were found either actually singing or calling when they were recorded"},{"metadata":{},"cell_type":"markdown","source":"### 12. Lets inspect volume,length and elevation\n\n* elevation: Height from Sea Level\n* length: This is the length of the Bird Call, not the length of the recording.\n* volume:"},{"metadata":{"trusted":true},"cell_type":"code","source":"# lets visualize elevation,volume,length\n\nfig = plt.figure(constrained_layout=True, figsize=(20, 9))\n\n# Creating a grid:\ngrid = gridspec.GridSpec(ncols=4, nrows=2, figure=fig)\n\n\n# sampling_rate.\nax1 = fig.add_subplot(grid[0, :2])\n\n# Plot the countplot.\nsns.countplot(train.length,\n              alpha=0.9,\n              ax=ax1,\n              palette=purple_black,\n              order = train.length.value_counts().sort_values(ascending = False).index)\n\nplt.xlabel(\"Length Distribution\")\nplt.ylabel(\"Count\")\nax1.set_title('Length')\n\n# volume              \nax2 = fig.add_subplot(grid[0, 2:])\n\nsns.countplot(train.volume,\n              alpha=0.9,\n              ax = ax2,\n              palette=purple_black,           \n              order = train.volume.value_counts().sort_values(ascending = False).index,   \n             )\nplt.xlabel(\"Volume Distribution\")\nplt.ylabel(\"Count\")\nax2.set_title('Volume')\n\n# elevation              \nax3 = fig.add_subplot(grid[1, :])\nsns.countplot(train.elevation,\n              alpha=0.9,\n              ax = ax3,\n              palette=purple_black,           \n              order = train.elevation.value_counts().sort_values(ascending = False).iloc[:15].index,   \n             )\nplt.xlabel(\"Elevation Distribution\")\nplt.ylabel(\"Count\")\nax3.set_title('Elevation')\n\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"📌 Key Observations:\n* Length & Volumne is not specified for majority of the recordings\n\n* Elevation is 0-10 for majortity of the records"},{"metadata":{},"cell_type":"markdown","source":"### 13. Lets inspect file type, license, bitrate_of_mp3\n\n* file_type - Audio File type and mostly every file is a mp3\n* license - License of the recording\n* birate_of_mp3 - Number of Bits used for encoding per second"},{"metadata":{"trusted":true},"cell_type":"code","source":"# lets visualize file type, license, bitrate_of_mp3\n\nfig = plt.figure(constrained_layout=True, figsize=(20, 9))\n\n# Creating a grid:\ngrid = gridspec.GridSpec(ncols=4, nrows=2, figure=fig)\n\n# playback used\nax1 = fig.add_subplot(grid[0, :2])\n\nsns.countplot(train.file_type,\n              alpha=0.9,\n              ax=ax1,\n              palette=purple_black,\n              order = train.file_type.value_counts().sort_values(ascending = False).index)\nplt.xlabel(\"File Type\")\nplt.ylabel(\"Count\")\nax1.set_title('File Type Distribution')\n\n\n# sampling_rate.\nax2 = fig.add_subplot(grid[0, 2:])\n\n# using short forms for liecense values\ntrain['license'] = train['license'].replace([\"Creative Commons Attribution-NonCommercial-ShareAlike 4.0\"],[\"CCA-NCSA4.0\"])\ntrain['license'] = train['license'].replace([\"Creative Commons Attribution-NonCommercial-ShareAlike 3.0\"],[\"CCA-NCSA3.0\"])\ntrain['license'] = train['license'].replace([\"Creative Commons Attribution-ShareAlike 3.0\"],[\"CCA-SA3.0\"])\ntrain['license'] = train['license'].replace([\"Creative Commons Attribution-ShareAlike 4.0\"],[\"CCA-SA4.0\"])\n                                          \n\n# Plot the countplot.\nsns.countplot(train.license,\n              alpha=0.9,\n              ax=ax2,\n              palette=purple_black,\n              order = train.license.value_counts().sort_values(ascending = False).index)\n\nplt.xlabel(\"License\")\nplt.ylabel(\"Count\")\nax2.set_title('License Distribution')\n\n# type              \nax3 = fig.add_subplot(grid[1, :])\n\nsns.countplot(train.bitrate_of_mp3,              \n              alpha=0.9,\n              ax = ax3,\n              palette=purple_black,           \n              order = train.bitrate_of_mp3.value_counts().sort_values(ascending = False).iloc[:10].index)\nplt.xlabel(\"Bitrate Of Mp3\")\nplt.ylabel(\"Count\")\nax3.set_title('Bitrate Of Mp3  Distribution')\n\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"📌 Key Observations:\n\n* Majority of the recordings are in mp3 format\n* First 2 category forms the majority for license type\n* 128000 bps is the most frequently used bitrate for recoding"},{"metadata":{},"cell_type":"markdown","source":"### 14. Lets inspect background, author & recordist\n\nrecordist: Name of the recordist\nbackground: Background Birds Identified\nauthor: Person who recorded the audio"},{"metadata":{"trusted":true},"cell_type":"code","source":"# lets visualize background\nplt.figure(constrained_layout=True, figsize=(12, 8))\n\ncount = train.background.value_counts().sort_values(ascending = False)[:20]\n\nfig = px.pie(count,\n             values=count.values,\n             names=count.index,\n             color_discrete_sequence=purple_black,\n             hole=.4)\nfig.update_traces(textinfo='percent', pull=0.05)\n\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# lets visualize author\nplt.figure(constrained_layout=True, figsize=(12, 8))\n\ncount = train.author.value_counts().sort_values(ascending = False)[:20]\n\nfig = px.pie(count,\n             values=count.values,\n             names=count.index,\n             color_discrete_sequence=purple_black,\n             hole=.4)\nfig.update_traces(textinfo='percent', pull=0.05)\n\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# lets visualize recordist\nplt.figure(constrained_layout=True, figsize=(12, 8))\n\ncount = train.recordist.value_counts().sort_values(ascending = False)[:20]\n\nfig = px.pie(count,\n             values=count.values,\n             names=count.index,\n             color_discrete_sequence=purple_black,\n             hole=.4)\nfig.update_traces(textinfo='percent', pull=0.05)\n\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Audio Files Analysis\n\n`Audio Signal`\n\n* The audio signal is a three-dimensional signal in which three axes represent time, amplitude and frequency.\n  It is a complex signal composed of multiple ‘single-frequency sound waves’ which travel together as a disturbance(pressure-change) in the medium. \n\n  When sound is recorded we only capture the resultant amplitudes of those multiple waves. \n\n\n\n![image.png](attachment:image.png)\n\n`Sampling`\n\n* Sound is a continuous wave. We can digitise sound by breaking the continuous wave into discrete signals. This process is called sampling. Sampling converts a sound wave into a sequence of samples or a discrete-time signal.\n\n`Sampling Rate (sr)`\n* The sampling rate is the number of samples per second. Hz or Hertz is the unit of the sampling rate. 20 kHz is the audible range for human beings.\n\n`Amplitudes`\n* From the definition of sound waves — This amplitude is actually the amplitude of air particles which are oscillating because of the pressure change in the atmosphere due to sound.\n\n* These amplitudes are not very informative, as they only talk about the loudness of audio recording. \n \n`Fourier Transform`\n\n* To better understand the audio signal, it is necessary to transform it into the frequency-domain. The frequency-domain representation of a signal tells us what different frequencies are present in the signal. \n\n* Fourier Transform is a mathematical concept that can convert a continuous signal from time-domain to frequency-domain. 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"}}},{"metadata":{},"cell_type":"markdown","source":"### LIBROSA\n\nWe will use `LibROSA` package to analyse audio files , it provides the building blocks necessary to create audio information retrieval systems.\n\nWe will also use `IPython.display` package to listen to audio files in the notebook."},{"metadata":{"trusted":true},"cell_type":"code","source":"print('Minimum samples per category = ', min(train.ebird_code.value_counts()))\nprint('Maximum samples per category = ', max(train.ebird_code.value_counts()))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"📌 Key Observations:\n* The number of audio samples per category is non-nform. The minimum number of audio samples in a category is 9 while the maximum is 100"},{"metadata":{},"cell_type":"markdown","source":"### 1. Let's listen to some music!"},{"metadata":{"trusted":true},"cell_type":"code","source":"perfal = '/kaggle/input/birdsong-recognition/train_audio/perfal/XC463087.mp3'   # Hi-hat\nipd.Audio(perfal)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"lotduc = '/kaggle/input/birdsong-recognition/train_audio/lotduc/XC121426.mp3'   # Hi-hat\nipd.Audio(lotduc)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"rewbla = '/kaggle/input/birdsong-recognition/train_audio/rewbla/XC135672.mp3'   # Hi-hat\nipd.Audio(rewbla)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"warvir = '/kaggle/input/birdsong-recognition/train_audio/warvir/XC192521.mp3'   # Hi-hat\nipd.Audio(warvir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"lecthr = '/kaggle/input/birdsong-recognition/train_audio/lecthr/XC141435.mp3'   # Hi-hat\nipd.Audio(lecthr)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"librosa.display is used to display the audio files in different formats such as `wave plot`, `spectrogram`, or `colormap` "},{"metadata":{"trusted":true},"cell_type":"markdown","source":"### 2. Waveplots,Spectogram, Mel-Spectorgram\n\n\n* `Waveplots` let us know the loudness of the audio at a given time. Waveplot is the time-domain representation of a given signal. \n    This shows us the loudness (amplitude) of sound wave changing with time. Here amplitude = 0 represents silence. \n\n\n* `Spectogram` is a visual representation of the spectrum of frequencies of sound or other signals as they vary with time. It’s a representation of frequencies changing with respect to time for given music signals (shows different frequencies playing at a particular time along with it’s amplitude)\n\n* `Mel-Spectogtram` it represents an acoustic time-frequency representation of a sound, it is a normal Spectrogram, but with a Mel Scale on the y axis"},{"metadata":{"trusted":true},"cell_type":"code","source":"def audioinfo(filename, species):   \n    # The load functions loads the audio file and converts it into an array of values which represent the amplitude if a sample at a \n    # given point of time.\n\n    data,sample_rate1 = librosa.load(filename, res_type='kaiser_best')\n\n    print(\"data:\",data,\"\\n\")\n    print(\"Sample Rate (KHz):\",sample_rate1)\n\n    # lenth of the audio\n    print('Audio Length:', np.shape(data)[0]/sample_rate1)\n    \n    # ----------------------------------------------------------WAVE PLOT-----------------------------------------------------------\n    plt.figure(figsize=(30,20))\n    plt.subplot(3,1,1)\n    \n    # Amplitude and frequency are important parameters of the sound and are unique for each audio. \n\n    # librosa.display.waveplot is used to plot waveform of amplitude vs time where the first axis is an amplitude and second axis is time\n   \n    librosa.display.waveplot(data,sr=sample_rate1,color = 'darkblue')\n    plt.xlabel(\"Time (seconds) -->\")\n    plt.ylabel(\"Amplitude\")\n    plt.title(\"Waveplot for - \" + species)\n    \n    # --------------------------------------------------------SPECTOGRAM------------------------------------------------------------\n    plt.subplot(3,1,2)\n     # .stft converts data into short term Fourier transform. STFT converts signal such that we can know the amplitude of given \n     # frequency at a given time. Using STFT we can determine the amplitude of various frequencies playing at a given time of an audio\n     # signal. \n    X = librosa.stft(data)\n\n    Xdb = librosa.amplitude_to_db(abs(X))\n\n    #.specshow is used to display spectogram.\n    librosa.display.specshow(Xdb, sr=sample_rate1, x_axis='time', y_axis='hz',cmap = 'winter') \n\n    plt.colorbar()\n    plt.xlabel(\"Time (seconds) -->\")\n    plt.ylabel(\"Amplitude\")\n    plt.title(\"Spectogram for - \" + species)\n    \n    # ----------------------------------------------------MEL SPECTOGRAM----------------------------------------------------------\n    plt.subplot(3,1,3)\n    librosa.feature.melspectrogram(y=data, sr=sample_rate1)\n\n    D = np.abs(librosa.stft(data))**2\n    S = librosa.feature.melspectrogram(S=D)\n    S = librosa.feature.melspectrogram(y=data, sr=sample_rate1)\n\n    librosa.display.specshow(librosa.power_to_db(S,ref=np.max),x_axis='time',cmap = 'rainbow')\n    plt.colorbar(format='%+2.0f dB')\n    plt.title(\"Mel spectrogram for species - \" + species)\n    plt.xlabel(\"Time (seconds) -->\")\n    plt.ylabel(\"Amplitude\")   \n     \n    plt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"audioinfo('/kaggle/input/birdsong-recognition/train_audio/perfal/XC463087.mp3',\"perfal\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"audioinfo('/kaggle/input/birdsong-recognition/train_audio/lotduc/XC121426.mp3',\"lotduc\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"audioinfo(\"/kaggle/input/birdsong-recognition/train_audio/rewbla/XC135672.mp3\",\"rewbla\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"audioinfo( '/kaggle/input/birdsong-recognition/train_audio/warvir/XC192521.mp3',\"warvir\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"audioinfo(\"/kaggle/input/birdsong-recognition/train_audio/lecthr/XC141435.mp3\",\"lecthr\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### 3.Zero Crossing Rate\n\n* The zero crossing rate indicates the number of times that a signal crosses the horizontal axis."},{"metadata":{"trusted":true},"cell_type":"code","source":"def zero_cross(filename):\n    data,sample_rate1 = librosa.load(filename)\n    # Zooming in\n    n0 = 9000\n    n1 = 9100\n    plt.figure(figsize=(20, 5))\n    plt.plot(data[n0:n1],color = \"gold\")\n    plt.grid()\n    \n    zero_crossings = librosa.zero_crossings(data, pad=False)\n    print(\"Zero Crossing Shape:\",zero_crossings.shape)\n    \n    print(\"Total Zero Crossings:\",sum(zero_crossings))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"zero_cross(perfal)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"zero_cross(lotduc)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"zero_cross(rewbla)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"zero_cross(warvir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"zero_cross(lecthr)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### 4. Spectral Centroid\n\n* The spectral centroid is a measure used in digital signal processing to characterise a spectrum. It indicates where the center of mass of the spectrum is located."},{"metadata":{"trusted":true},"cell_type":"code","source":"def spectral_centroid(filename):\n    data,sample_rate1 = librosa.load(filename)\n    \n    spectral_centroids = librosa.feature.spectral_centroid(data, sr=sample_rate1)[0]\n    spectral_centroids.shape\n\n    # Computing the time variable for visualization\n    plt.figure(figsize=(20,5))\n    frames = range(len(spectral_centroids))\n    t = librosa.frames_to_time(frames)\n\n    # Normalising the spectral centroid for visualisation\n    def normalize(data, axis=0):\n        return sklearn.preprocessing.minmax_scale(data, axis=axis)\n\n    #Plotting the Spectral Centroid along the waveform\n    librosa.display.waveplot(data, sr=sample_rate1, alpha=0.4)\n    plt.plot(t, normalize(spectral_centroids), color='r')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"spectral_centroid(perfal)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"spectral_centroid(lotduc)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"spectral_centroid(rewbla)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"spectral_centroid(warvir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"spectral_centroid(lecthr)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### 5. Spectral Rolloff\n\nSpectral rolloff is the frequency below which a specified percentage of the total spectral energy, e.g. 85%, lies."},{"metadata":{"trusted":true},"cell_type":"code","source":"def rolloff(filename):\n    data,sample_rate1 = librosa.load(filename)\n    \n    spectral_centroids = librosa.feature.spectral_centroid(data, sr=sample_rate1)[0]\n    frames = range(len(spectral_centroids))\n    t = librosa.frames_to_time(frames)\n    \n    def normalize(data, axis=0):\n        return sklearn.preprocessing.minmax_scale(data, axis=axis)\n\n    plt.figure(figsize=(20,5))\n    spectral_rolloff = librosa.feature.spectral_rolloff(data+0.01, sr=sample_rate1)[0]\n    librosa.display.waveplot(data, sr=sample_rate1, alpha=0.4)\n    plt.plot(t, normalize(spectral_rolloff), color='g')\n    plt.grid()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"rolloff(perfal)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"rolloff(lotduc)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"rolloff(rewbla)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"rolloff(warvir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"rolloff(lecthr)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### 6. MFCC\n* Mel-frequency cepstral coefficients (MFCCs) are coefficients that collectively make up an MFC. They are derived from a type of cepstral representation of the audio clip (a nonlinear \"spectrum-of-a-spectrum\""},{"metadata":{"trusted":true},"cell_type":"code","source":"# MFCC\ndef mfcc(filename):\n    data,sample_rate1 = librosa.load(filename)\n    plt.figure(figsize=(20,5))\n    mfccs = librosa.feature.mfcc(data, sr=sample_rate1)\n    print(mfccs.shape)\n\n    librosa.display.specshow(mfccs, sr=sample_rate1, x_axis='time')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mfcc(perfal)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mfcc(lotduc)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mfcc(rewbla)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mfcc(warvir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mfcc(lecthr)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### 7. Chrome Frequencies\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"def chrom_freq(filename):\n    data,sample_rate1 = librosa.load(filename)\n    \n    hop_length = 512\n    chromagram = librosa.feature.chroma_cqt(data, sr=sample_rate1, hop_length=hop_length)\n    plt.figure(figsize=(15, 5))\n    librosa.display.specshow(chromagram, x_axis='time', y_axis='chroma', hop_length=hop_length)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"chrom_freq(perfal)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"chrom_freq(lotduc)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"chrom_freq(rewbla)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"chrom_freq(warvir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"chrom_freq(lecthr)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### 8. Fundamental Frequency Estimation using probabilistic YIN algo**\n\nUsed to calculate the fundamental frequency curve from given audio input"},{"metadata":{"trusted":true},"cell_type":"code","source":"def fundamental_frequency(filename):\n    y, sr = librosa.load(filename)\n    f0, voiced_flag, voiced_probs = librosa.pyin(y, fmin=librosa.note_to_hz('C2'), fmax=librosa.note_to_hz('C7'))\n    times = librosa.times_like(f0)\n    D = librosa.amplitude_to_db(np.abs(librosa.stft(y)), ref=np.max)\n    fig, ax = plt.subplots()\n    img = librosa.display.specshow(D, x_axis='time', y_axis='log', ax=ax)\n    ax.set(title='pYIN fundamental frequency estimation')\n    fig.colorbar(img, ax=ax, format=\"%+2.f dB\")\n    ax.plot(times, f0, label='f0', color='cyan', linewidth=3)\n    ax.legend(loc='upper right')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fundamental_frequency(perfal)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fundamental_frequency(warvir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fundamental_frequency(lotduc)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fundamental_frequency(lecthr)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fundamental_frequency(rewbla)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### 9. Rhythm Fetaure\nTempogram - local autocorrelation of the onset strength envelope"},{"metadata":{"trusted":true},"cell_type":"code","source":"def compute_tempogram(filename):\n    # computing local onset autocorrelation\n    y,sr = librosa.load(filename)\n    hop_length = 512\n    oenv = librosa.onset.onset_strength(y=y, sr=sr, hop_length=hop_length)\n    tempogram = librosa.feature.tempogram(onset_envelope=oenv, sr=sr,\n                                      hop_length=hop_length)\n    \n    # Computing global onset autocorrelation\n    ac_global = librosa.autocorrelate(oenv, max_size=tempogram.shape[0])\n    ac_global = librosa.util.normalize(ac_global)\n    \n    # Estimating global tempo\n    tempo = librosa.beat.tempo(onset_envelope=oenv, sr=sr,\n                           hop_length=hop_length)[0]\n    \n    # plotting\n    \n    fig, ax = plt.subplots(nrows=4, figsize=(10, 10))\n    times = librosa.times_like(oenv, sr=sr, hop_length=hop_length)\n    ax[0].plot(times, oenv, label='Onset strength')\n    ax[0].label_outer()\n    ax[0].legend(frameon=True)\n    librosa.display.specshow(tempogram, sr=sr, hop_length=hop_length,\n                             x_axis='time', y_axis='tempo', cmap='magma',\n                             ax=ax[1])\n    ax[1].axhline(tempo, color='w', linestyle='--', alpha=1,\n                label='Estimated tempo={:g}'.format(tempo))\n    ax[1].legend(loc='upper right')\n    ax[1].set(title='Tempogram')\n    x = np.linspace(0, tempogram.shape[0] * float(hop_length) / sr,\n                    num=tempogram.shape[0])\n    ax[2].plot(x, np.mean(tempogram, axis=1), label='Mean local autocorrelation')\n    ax[2].plot(x, ac_global, '--', alpha=0.75, label='Global autocorrelation')\n    ax[2].set(xlabel='Lag (seconds)')\n    ax[2].legend(frameon=True)\n    freqs = librosa.tempo_frequencies(tempogram.shape[0], hop_length=hop_length, sr=sr)\n    ax[3].semilogx(freqs[1:], np.mean(tempogram[1:], axis=1),\n                 label='Mean local autocorrelation', basex=2)\n    ax[3].semilogx(freqs[1:], ac_global[1:], '--', alpha=0.75,\n                 label='Global autocorrelation', basex=2)\n    ax[3].axvline(tempo, color='black', linestyle='--', alpha=.8,\n                label='Estimated tempo={:g}'.format(tempo))\n    ax[3].legend(frameon=True)\n    ax[3].set(xlabel='BPM')\n    ax[3].grid(True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"compute_tempogram(lecthr)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"compute_tempogram(warvir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"compute_tempogram(perfal)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"compute_tempogram(lotduc)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### 10. Harmonic-percussive source separaton from audio input\n\nhpss: It will decompose an audio time series into harmonic and percussive components."},{"metadata":{"trusted":true},"cell_type":"code","source":"def decompose_audio(filename):\n    y,sr = librosa.load(filename)\n    D = librosa.stft(y)\n    #y_harmonic, y_percussive = librosa.effects.hpss(D, margin=(1.0,5.0)) # we will get more isolated percussive component by increasing margin \n    D_harmonic, D_percussive = librosa.decompose.hpss(D)\n    # Pre-compute a global reference power from the input spectrum\n    rp = np.max(np.abs(D))\n\n    plt.figure(figsize=(12, 8))\n\n    plt.subplot(3, 1, 1)\n    librosa.display.specshow(librosa.amplitude_to_db(D, ref=rp), y_axis='log')\n    plt.colorbar()\n    plt.title('Full spectrogram')\n\n    plt.subplot(3, 1, 2)\n    librosa.display.specshow(librosa.amplitude_to_db(D_harmonic, ref=rp), y_axis='log')\n    plt.colorbar()\n    plt.title('Harmonic spectrogram')\n\n    plt.subplot(3, 1, 3)\n    librosa.display.specshow(librosa.amplitude_to_db(D_percussive, ref=rp), y_axis='log', x_axis='time')\n    plt.colorbar()\n    plt.title('Percussive spectrogram')\n    plt.tight_layout()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"decompose_audio(perfal)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"decompose_audio(lotduc)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"decompose_audio(warvir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"decompose_audio(lecthr)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Audio Augmentation"},{"metadata":{},"cell_type":"markdown","source":"## Change pitch and speed"},{"metadata":{"trusted":true},"cell_type":"code","source":"def pitch_speed(filename):\n    data, sr = librosa.load(filename)\n    pitch_speed = data.copy()\n    length_change = np.random.uniform(low=0.8, high = 1)\n    speed_fac = 1.0  / length_change\n    print(\"resample length_change = \",length_change)\n    tmp = np.interp(np.arange(0,len(pitch_speed),speed_fac),np.arange(0,len(pitch_speed)),pitch_speed)\n    minlen = min(pitch_speed.shape[0], tmp.shape[0])\n    pitch_speed *= 0\n    pitch_speed[0:minlen] = tmp[0:minlen]\n    librosa_display.waveplot(data, sr=sr, alpha=0.5)\n    librosa_display.waveplot(pitch_speed, sr=sr, color='r', alpha=0.25)\n    plt.title('augmented pitch and speed')\n    return ipd.Audio(data, rate=sr)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pitch_speed(perfal)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pitch_speed(lotduc)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pitch_speed(rewbla)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pitch_speed(warvir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pitch_speed(lecthr)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Change pitch only"},{"metadata":{"trusted":true},"cell_type":"code","source":"def pitch(filename):\n    data, sr = librosa.load(filename)\n    y_pitch = data.copy()\n    bins_per_octave = 12\n    pitch_pm = 2\n    pitch_change =  pitch_pm * 2*(np.random.uniform())   \n    print(\"pitch_change = \",pitch_change)\n    y_pitch = librosa.effects.pitch_shift(y_pitch.astype('float64'), \n                                          sr, n_steps=pitch_change, \n                                          bins_per_octave=bins_per_octave)\n    librosa_display.waveplot(data, sr=sr, alpha=0.5)\n    librosa_display.waveplot(y_pitch, sr=sr, color='r', alpha=0.25)\n    plt.title('augmented pitch only')\n    plt.tight_layout()\n    plt.show()\n    return ipd.Audio(data, rate=sr)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pitch(perfal)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pitch(lotduc)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pitch(rewbla)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pitch(warvir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pitch(lecthr)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Change speed only"},{"metadata":{"trusted":true},"cell_type":"code","source":"def speed(filename):\n    data, sr = librosa.load(filename)\n    aug = naa.SpeedAug()\n    augmented_data = aug.augment(data)\n\n    librosa_display.waveplot(data, sr=sr, alpha=0.5)\n    librosa_display.waveplot(augmented_data, sr=sr, color='r', alpha=0.25)\n    plt.title('augmented speed only')\n    plt.tight_layout()\n    plt.show()\n    return ipd.Audio(augmented_data, rate=sr)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"speed(perfal)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"speed(lotduc)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"speed(rewbla)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"speed(warvir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"speed(lecthr)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## value augmentation"},{"metadata":{"trusted":true},"cell_type":"code","source":"def augmentation(filename):\n    data, sr = librosa.load(filename)\n    y_aug = data.copy()\n    dyn_change = np.random.uniform(low=1.5,high=3)\n    print(\"dyn_change = \",dyn_change)\n    y_aug = y_aug * dyn_change\n    print(y_aug[:50])\n    print(data[:50])\n    librosa_display.waveplot(data, sr=sr, alpha=0.5)\n    librosa_display.waveplot(y_aug, sr=sr, color='r', alpha=0.25)\n    plt.title('amplify value')\n    return ipd.Audio(y_aug, rate=sr)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"augmentation(perfal)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"augmentation(lotduc)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"augmentation(rewbla)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"augmentation(warvir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"augmentation(lecthr)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Adding Noise"},{"metadata":{"trusted":true},"cell_type":"code","source":"def add_noise(filename):\n    data, sr = librosa.load(filename)\n    y_noise = data.copy()\n    # you can take any distribution from https://docs.scipy.org/doc/numpy-1.13.0/reference/routines.random.html\n    noise_amp = 0.005*np.random.uniform()*np.amax(y_noise)\n    y_noise = y_noise.astype('float64') + noise_amp * np.random.normal(size=y_noise.shape[0])\n    librosa_display.waveplot(data, sr=sr, alpha=0.5)\n    librosa_display.waveplot(y_noise, sr=sr, color='r', alpha=0.25)\n    return ipd.Audio(y_noise, rate=sr)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"add_noise(perfal)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"add_noise(lotduc)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"add_noise(rewbla)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"add_noise(warvir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"add_noise(lecthr)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## random shifting"},{"metadata":{"trusted":true},"cell_type":"code","source":"def random_shift(filename):\n    data, sr = librosa.load(filename)\n    y_shift = data.copy()\n    timeshift_fac = 0.2 *2*(np.random.uniform()-0.5)  # up to 20% of length\n    print(\"timeshift_fac = \",timeshift_fac)\n    start = int(y_shift.shape[0] * timeshift_fac)\n    print(start)\n    if (start > 0):\n        y_shift = np.pad(y_shift,(start,0),mode='constant')[0:y_shift.shape[0]]\n    else:\n        y_shift = np.pad(y_shift,(0,-start),mode='constant')[0:y_shift.shape[0]]\n    librosa_display.waveplot(data, sr=sr, alpha=0.5)\n    librosa_display.waveplot(y_shift, sr=sr, color='r', alpha=0.25)\n    return ipd.Audio(y_shift, rate=sr)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"random_shift(perfal)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"random_shift(lotduc)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"random_shift(rewbla)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"random_shift(warvir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"random_shift(lecthr)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Applying hpss"},{"metadata":{"trusted":true},"cell_type":"code","source":"def hpss(filename):\n    data, sr = librosa.load(filename)\n    y_hpss = librosa.effects.hpss(data.astype('float64'))\n    print(y_hpss[1][:10])\n    print(data[:10])\n    librosa_display.waveplot(data, sr=sr, alpha=0.5)\n    librosa_display.waveplot(y_hpss[1], sr=sr, color='r', alpha=0.25)\n    plt.title('apply hpss')\n    return ipd.Audio(y_hpss[1], rate=sr)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"hpss(perfal)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"hpss(lotduc)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"hpss(rewbla)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"hpss(warvir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"hpss(lecthr)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Streching"},{"metadata":{"trusted":true},"cell_type":"code","source":"def streching(filename):\n    data, sr = librosa.load(filename)\n    input_length = len(data)\n    streching = data.copy()\n    streching = librosa.effects.time_stretch(streching.astype('float'), 1.1)\n    if len(streching) > input_length:\n        streching = streching[:input_length]\n    else:\n        streching = np.pad(streching, (0, max(0, input_length - len(streching))), \"constant\")\n    librosa_display.waveplot(data, sr=sr, alpha=0.5)\n    librosa_display.waveplot(streching, sr=sr, color='r', alpha=0.25)\n    \n    plt.title('stretching')\n    return ipd.Audio(streching, rate=sr)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"streching(perfal)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"streching(lotduc)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":" streching(rewbla)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":" streching(warvir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"streching(lecthr)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## cropping"},{"metadata":{"trusted":true},"cell_type":"code","source":"def crop(filename):\n    data, sr = librosa.load(filename)\n    aug = naa.CropAug(sampling_rate=sr)\n    augmented_data = aug.augment(data)\n\n    librosa_display.waveplot(augmented_data, sr=sr, alpha=0.5)\n    librosa_display.waveplot(data, sr=sr, color='r', alpha=0.25)\n\n    plt.tight_layout()\n    plt.show()\n\n    return ipd.Audio(augmented_data, rate=sr)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"crop(perfal) ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"crop(lotduc)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"crop(rewbla)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"crop(warvir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"crop(lecthr)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Loudness Augmentation"},{"metadata":{"trusted":true},"cell_type":"code","source":"def loudnessaug(filename):\n    data, sr = librosa.load(filename)\n    aug = naa.LoudnessAug(loudness_factor=(2, 5))\n    augmented_data = aug.augment(data)\n\n    librosa_display.waveplot(augmented_data, sr=sr, alpha=0.25)\n    librosa_display.waveplot(data, sr=sr, color='r', alpha=0.5)\n\n    plt.tight_layout()\n    plt.show()\n\n    return ipd.Audio(augmented_data,rate=sr)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"loudnessaug(perfal) ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"loudnessaug(lotduc)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"loudnessaug(rewbla)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"loudnessaug(warvir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"loudnessaug(lecthr)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Mask Augmentation"},{"metadata":{"trusted":true},"cell_type":"code","source":"def mask(filename):\n    data, sr = librosa.load(filename)\n    aug = naa.MaskAug(sampling_rate=sr, mask_with_noise=False)\n    augmented_data = aug.augment(data)\n\n    librosa_display.waveplot(data, sr=sr, alpha=0.5)\n    librosa_display.waveplot(augmented_data, sr=sr, color='r', alpha=0.25)\n\n    plt.tight_layout()\n    plt.show()\n    \n    return ipd.Audio(augmented_data, rate=sr)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mask(perfal)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mask(lotduc)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mask(rewbla)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mask(warvir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mask(lecthr)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Shift Augmentation"},{"metadata":{"trusted":true},"cell_type":"code","source":"def shift(filename):\n    data, sr = librosa.load(filename)\n    aug = naa.ShiftAug(sampling_rate=sr)\n    augmented_data = aug.augment(data)\n\n    librosa_display.waveplot(data, sr=sr, alpha=0.5)\n    librosa_display.waveplot(augmented_data, sr=sr, color='r', alpha=0.25)\n\n    plt.tight_layout()\n    plt.show()\n    \n    return ipd.Audio(augmented_data, rate=sr)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"shift(perfal)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"shift(lotduc)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"shift(rewbla)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"shift(warvir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"shift(lecthr)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# **Model Pre-Processing**"},{"metadata":{},"cell_type":"markdown","source":"1. Now we will prepare adict with unique birds and Key into the dict"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_set= train.copy()\nbirds_key=train[\"ebird_code\"].unique()\nbirds_key","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"random.shuffle(birds_key)\ntrain_set = train_set.query(\"ebird_code in @birds_key\")\n\nidBirdDict = {}\nebirdDict = {}\nebirdDict[\"nocall\"] = 0\nidBirdDict[0] = \"nocall\"\nfor idx, unique_ebird_code in enumerate(train_set.ebird_code.unique()):\n    ebirdDict[unique_ebird_code] = str(idx+1)\n    idBirdDict[idx+1] = str(unique_ebird_code)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ebirdDict","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"idBirdDict","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Let create a Sample Set as Whote data set will run for long hours\nsample_set=pd.DataFrame(columns=['ebird_code','audio_File_path',\"song_sample\",\"bird\"])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Using Francois's code to extract the data/ run model\n\ndef get_sample(filename, bird, sample_set):\n    min_max_Scaler=MinMaxScaler()\n    wave_data, wave_rate = librosa.load(filename)\n    data_point_per_second = 10\n    \n    #Take 10 data points every second\n    prepared_sample = wave_data[0::int(wave_rate/data_point_per_second)]\n    #We normalize each sample before extracting 5s samples from it\n    normalized_sample = min_max_Scaler.fit_transform(prepared_sample.reshape(-1, 1))\n    normalized_sample = normalized_sample.flatten()\n    \n    #only take 5s samples and add them to the dataframe\n    song_sample = []\n    sample_length = 5*data_point_per_second\n    for idx in range(0,len(normalized_sample),sample_length): \n        song_sample = normalized_sample[idx:idx+sample_length]\n        if len(song_sample)>=sample_length:\n            sample_set = sample_set.append({\"song_sample\":np.asarray(song_sample).astype(np.float32),\n                                            \"bird\":ebirdDict[bird],\n                                           \"audio_File_path\":filename,\n                                           \"ebird_code\":bird}, \n                                           ignore_index=True)\n                     \n    return sample_set","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# we will run for 5000 records for total Trains set to prepare for Model \nwith tqdm(total=5000) as pbar:\n    for idx, row in train_set[:5000].iterrows():\n        pbar.update(1)\n        #print(idx)\n        sample_set = get_sample(row.audio_File_path, row.ebird_code, sample_set)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Now out of the complete sequence length we will choose with the fixed 50 sequence length for the above input array on Sample Set\n# also divide the sample set into train and val set on the basis of 80:20\nsequence_length = 50\nsplit_per = 0.80\ntrain_item_count = int(len(sample_set)*split_per)\nval_item_count = len(sample_set)-int(len(sample_set)*split_per)\ntraining_set = sample_set[:train_item_count]\nvalidation_set = sample_set[train_item_count:]","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**LSTM Model**"},{"metadata":{"trusted":true},"cell_type":"code","source":"# we will have Sequential LSTM with dropout and 3 layer as SOftMax and Optimizer is ADAM\nmodel = Sequential()\nmodel.add(LSTM(32, return_sequences=True, recurrent_dropout=0.2,input_shape=(None, sequence_length)))\nmodel.add(LSTM(32,recurrent_dropout=0.2))\nmodel.add(Dense(128,activation = 'relu'))\nmodel.add(Dropout(0.3))\nmodel.add(Dense(128,activation = 'relu'))\nmodel.add(Dropout(0.3))\nmodel.add(Dense(len(ebirdDict.keys()), activation=\"softmax\"))\n\nmodel.summary()\n\ncallbacks = [ReduceLROnPlateau(monitor='val_loss', patience=2, verbose=1, factor=0.7),\n             EarlyStopping(monitor='val_loss', patience=10),\n             ModelCheckpoint(filepath='best_model.h5', monitor='val_loss', save_best_only=True)]\nmodel.compile(loss=\"categorical_crossentropy\", optimizer='adam')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Take the Xtrain and Y train from train Set from Sample Set data frame to be feed into LSTM Model\nX_train = np.asarray(np.reshape(np.asarray([np.asarray(x) for x in training_set[\"song_sample\"]]),(train_item_count,1,sequence_length))).astype(np.float32)\ntrain_gd = np.asarray([np.asarray(x) for x in training_set[\"bird\"]]).astype(np.float32)\nY_train = to_categorical(\n                train_gd, num_classes=len(ebirdDict.keys()), dtype='float32'\n            )\n\n\nX_val = np.asarray(np.reshape(np.asarray([np.asarray(x) for x in validation_set[\"song_sample\"]]),(val_item_count,1,sequence_length))).astype(np.float32)\nval_gd = np.asarray([np.asarray(x) for x in validation_set[\"bird\"]]).astype(np.float32)\nY_val = to_categorical(\n                val_gd, num_classes=len(ebirdDict.keys()), dtype='float32'\n            )","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Fit the LSTM model and plot the Train and validation Loss for 100 Epochs and batch Size of 32\nmodel_his1 = model.fit(X_train, Y_train, \n          epochs = 100, \n          batch_size = 32, \n          validation_data=(X_val, Y_val), \n          callbacks=callbacks)\n\nplt.plot(model_his1.history['loss'])\nplt.plot(model_his1.history['val_loss'])\nplt.title('Loss over epochs')\nplt.ylabel('Loss')\nplt.xlabel('Epoch')\nplt.legend(['Train', 'Validation'], loc='best')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# make the predictions function to predict on Unsenn data from the Model trained\nmodel.load_weights(\"best_model.h5\")\n\ndef make_prediction(df, audio_file_path):\n        \n    loaded_audio_sample = []\n    previous_filename = \"\"\n    data_point_per_second = 10\n    sample_length = 5*data_point_per_second\n    wave_data = []\n    wave_rate = None\n    \n    for idx,row in df.iterrows():\n        if previous_filename == \"\" or previous_filename!=row.filename:\n            filename = '{}/{}.mp3'.format(audio_file_path, row.filename)\n            wave_data, wave_rate = librosa.load(filename)\n            sample = wave_data[0::int(wave_rate/data_point_per_second)]\n        previous_filename = row.filename\n        \n        #basically allows to check if we are running the examples or the test set.\n        if \"site\" in df.columns:\n            if row.site==\"site_1\" or row.site==\"site_2\":\n                song_sample = np.array(sample[int(row.seconds-5)*data_point_per_second:int(row.seconds)*data_point_per_second])\n            elif row.site==\"site_3\":\n                #for now, I only take the first 5s of the samples from site_3 as they are groundtruthed at file level\n                song_sample = np.array(sample[0:sample_length])\n        else:\n            #same as the first condition but I isolated it for later and it is for the example file\n            song_sample = np.array(sample[int(row.seconds-5)*data_point_per_second:int(row.seconds)*data_point_per_second])\n\n        input_data = np.reshape(np.asarray([song_sample]),(1,sequence_length)).astype(np.float32)\n        prediction = model.predict(np.array([input_data]))\n        predicted_bird = idBirdDict[np.argmax(prediction)]\n\n        df.at[idx,\"birds\"] = predicted_bird\n    return df","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Let see how our model performs on example set given\nexample_set = pd.read_csv(base_path+\"example_test_audio_summary.csv\")\nexample_set[\"filename\"] = [ \"BLKFR-10-CPL_20190611_093000.pt540\" if filename==\"BLKFR-10-CPL\" else \"ORANGE-7-CAP_20190606_093000.pt623\" for filename in example_set[\"filename\"]]\nexample_set\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"example_audio_file_path = base_path +\"example_test_audio\"\nif os.path.exists(example_audio_file_path):\n    example_set = make_prediction(example_set, example_audio_file_path)\nexample_set","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Now lets predict on the test Set and prepare the Submission File\ntest_audio_file_path = base_path+\"test_audio/\"\nsubmission_set = pd.read_csv(base_path+\"sample_submission.csv\")\nsubmission_set.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"if os.path.exists(test_audio_file_path):\n    submission_set = make_prediction(test, test_audio_file_path)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_set[:20]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_set.to_csv(\"submission.csv\", index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_set","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}