{"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_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"\n🐀EDA|🐦Visualizations + Audio Exploration πŸ”‰πŸ”‰ \n\nCreated By Burhanuddin Latsaheb \n\n\n\n#
🐀EDA|🐦Visualizations + Audio Exploration πŸ”‰πŸ”‰
\n\n\n
If you find this notebook useful,support with an upvoteπŸ‘πŸ‘
\n","metadata":{}},{"cell_type":"markdown","source":"#

INTRODUCTION

\n \nNotebook Overview : \n\n\n1. An extensive EDA of the different Bird sounds and its different characteristics \n2. The goal of this competition is to use machine learning to identify Eastern African bird species by sound.\n\n\nIntroduction to the BirdCLEF 2023 Competition:\n\n\nBirds are not only fascinating creatures, but also an important indicator of the health of our environment. The BirdCLEF 2023 competition, hosted by Kaggle, aims to promote research and innovation in the field of bird species identification through acoustic signals. \n\n\nIn this competition, participants are challenged to develop algorithms that can accurately identify bird species from audio recordings. The dataset consists of over 1 million audio recordings from around the world, with a total duration of over 1,000 hours. Each recording is labeled with the corresponding bird species, and participants are tasked with developing a machine learning model that can accurately classify the species in new, unseen recordings.\n\n\nThe BirdCLEF 2023 competition provides a unique opportunity for researchers and data scientists to advance the field of bioacoustics and contribute to the preservation of bird populations around the world. Whether you are an experienced machine learning practitioner or just getting started, this competition offers a challenging and rewarding opportunity to make a real-world impact.","metadata":{}},{"cell_type":"markdown","source":"\n#

TABLE OF CONTENTS

\n- [1. Imports πŸ“‚](#1)\n- [2. EDA πŸ“Š](#2)\n * [Train Meta Data Info](#2.1)\n * [Plots](#2.2)\n * [Correlation Plots](#2.4)\n * [Interactive Map Plots](#2.3)\n * [Scatter Plot](#2.3.1)\n * [Map Box (Open Street Map)](#2.3.2)\n * [Map Box(Terrain View)](#2.3.3)\n * [EBird Taxonomy](#2.5)\n- [3. AUDIO EXPLORATION πŸ”‰πŸ”‰](#3)\n * [Helper Function](#3.1)\n * [Audio Exploration](#3.2)\n * [Black-and-white Mannikin](#3.2.1)\n * [African Black-headed Oriole](#3.2.2)\n * [African Bare-eyed Thrush](#3.2.3)\n * [African Gray Flycatcher](#3.2.4)\n * [African Goshawk](#3.2.5)\n","metadata":{}},{"cell_type":"markdown","source":"\n#

1. IMPORTSπŸ“‚

\n\n\n#### [Top ↑](#top)","metadata":{}},{"cell_type":"code","source":"import ast\nimport librosa\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport plotly.express as px\nimport plotly.graph_objs as go\nfrom collections import Counter\nimport matplotlib.pyplot as plt\nfrom IPython.display import Audio","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-03-08T07:38:11.474081Z","iopub.execute_input":"2023-03-08T07:38:11.475760Z","iopub.status.idle":"2023-03-08T07:38:11.483747Z","shell.execute_reply.started":"2023-03-08T07:38:11.475672Z","shell.execute_reply":"2023-03-08T07:38:11.482087Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n#

2. πŸ“ŠEDA πŸ“Š

\n\n\n#### [Top ↑](#top)","metadata":{}},{"cell_type":"markdown","source":"
\n πŸ’‘  Observations in Train Meta Data:
\n * There are total of 12 columns and 16941 rows in train meta data.
\n* Train data contains 16941 values with 454(0.2%) of missing values.
\n* There are total of 12 columns : 3 numerical , 9 categorical
\n* 454 Missing values in the train meta data.
\n
","metadata":{}},{"cell_type":"code","source":"trainmeta_df = pd.read_csv(\"/kaggle/input/birdclef-2023/train_metadata.csv\")\ntrainmeta_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-03-08T07:12:44.446156Z","iopub.execute_input":"2023-03-08T07:12:44.446528Z","iopub.status.idle":"2023-03-08T07:12:44.550205Z","shell.execute_reply.started":"2023-03-08T07:12:44.446493Z","shell.execute_reply":"2023-03-08T07:12:44.548730Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x = trainmeta_df[trainmeta_df[\"primary_label\"] == \"afgfly1\"]\nx","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n

Train Meta Data Info

\n\n#### [Top ↑](#top)","metadata":{}},{"cell_type":"markdown","source":"
\n πŸ’‘πŸ’‘  Further Insights of train dataset
\n * Latitude & Longitude have 227(1.34%) Missing Values
\n * Longitude & Rating are skewed
\n * Primary labels , Secondary Labels , Type , Scientific Name , Common Name , Author ,Filename have high Cardinality
\n
\n","metadata":{}},{"cell_type":"code","source":"print(trainmeta_df.info())","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-03-08T07:19:11.142989Z","iopub.execute_input":"2023-03-08T07:19:11.143739Z","iopub.status.idle":"2023-03-08T07:19:11.172209Z","shell.execute_reply.started":"2023-03-08T07:19:11.143649Z","shell.execute_reply":"2023-03-08T07:19:11.170642Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n

Plots

\n\n#### [Top ↑](#top)","metadata":{}},{"cell_type":"code","source":"fig = px.histogram(trainmeta_df, x=\"primary_label\", nbins=len(trainmeta_df[\"primary_label\"].unique()))\nfig.update_layout(title_text=\"Distribution of Primary Labels\")\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-08T07:27:10.229488Z","iopub.execute_input":"2023-03-08T07:27:10.230588Z","iopub.status.idle":"2023-03-08T07:27:10.800684Z","shell.execute_reply.started":"2023-03-08T07:27:10.230527Z","shell.execute_reply":"2023-03-08T07:27:10.799352Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"secondary_df = trainmeta_df['secondary_labels'].str.replace('[','').str.replace(']','').str.replace('\\'','').str.split(',', expand=True).stack().reset_index(level=1, drop=True).rename('secondary_label')\nsecondary_df = pd.merge(trainmeta_df.drop(columns=['secondary_labels']), secondary_df, left_index=True, right_index=True)\n\nfig = px.histogram(secondary_df, x=\"secondary_label\", title=\"Distribution of Secondary Labels\")\nfig.update_layout(xaxis_title=\"Secondary Label\", yaxis_title=\"Count\")\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-08T07:27:18.922302Z","iopub.execute_input":"2023-03-08T07:27:18.922782Z","iopub.status.idle":"2023-03-08T07:27:19.135530Z","shell.execute_reply.started":"2023-03-08T07:27:18.922741Z","shell.execute_reply":"2023-03-08T07:27:19.134342Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Flatten the list of labels in the \"type\" column\nlabels = [label.strip(\"[]'\") for sublist in trainmeta_df['type'].apply(ast.literal_eval) for label in sublist]\n\n# Count the occurrence of each label\nlabel_counts = Counter(labels)\n\n# Create a bar plot of the label counts\nfig = px.bar(x=list(label_counts.keys()), y=list(label_counts.values()))\nfig.update_layout(title_text=\"Distribution of Types\")\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-08T07:27:26.129399Z","iopub.execute_input":"2023-03-08T07:27:26.129932Z","iopub.status.idle":"2023-03-08T07:27:26.352224Z","shell.execute_reply.started":"2023-03-08T07:27:26.129885Z","shell.execute_reply":"2023-03-08T07:27:26.350862Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig1 = px.box(trainmeta_df, y=\"latitude\")\nfig1.update_layout(title_text=\"Distribution of Latitude\",\n yaxis=dict(title=\"Latitude\"))\n\n# create a box plot for longitude\nfig2 = px.box(trainmeta_df, y=\"longitude\")\nfig2.update_layout(title_text=\"Distribution of Longitude\",\n yaxis=dict(title=\"Longitude\"))\n\n# create a box plot for rating\nfig3 = px.box(trainmeta_df, y=\"rating\")\nfig3.update_layout(title_text=\"Distribution of Ratings\",\n yaxis=dict(title=\"Rating\"))\n\n# show the figures\nfig1.show()\nfig2.show()\nfig3.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-08T07:27:33.012501Z","iopub.execute_input":"2023-03-08T07:27:33.013248Z","iopub.status.idle":"2023-03-08T07:27:33.195810Z","shell.execute_reply.started":"2023-03-08T07:27:33.013197Z","shell.execute_reply":"2023-03-08T07:27:33.194441Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig = px.histogram(trainmeta_df, x=\"scientific_name\", nbins=len(trainmeta_df[\"scientific_name\"].unique()))\nfig.update_layout(title_text=\"Distribution of Scientific Names\")\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-08T07:27:43.370548Z","iopub.execute_input":"2023-03-08T07:27:43.370996Z","iopub.status.idle":"2023-03-08T07:27:43.504732Z","shell.execute_reply.started":"2023-03-08T07:27:43.370959Z","shell.execute_reply":"2023-03-08T07:27:43.503438Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\nfig = px.histogram(trainmeta_df, x=\"common_name\", nbins=len(trainmeta_df[\"common_name\"].unique()))\nfig.update_layout(title_text=\"Distribution of Common Names\")\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-08T07:27:53.932176Z","iopub.execute_input":"2023-03-08T07:27:53.933390Z","iopub.status.idle":"2023-03-08T07:27:54.064382Z","shell.execute_reply.started":"2023-03-08T07:27:53.933320Z","shell.execute_reply":"2023-03-08T07:27:54.063321Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig = px.histogram(trainmeta_df, x=\"author\", nbins=len(trainmeta_df[\"author\"].unique()))\nfig.update_layout(title_text=\"Distribution of Authors\")\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-08T07:27:58.591349Z","iopub.execute_input":"2023-03-08T07:27:58.591923Z","iopub.status.idle":"2023-03-08T07:27:58.728538Z","shell.execute_reply.started":"2023-03-08T07:27:58.591875Z","shell.execute_reply":"2023-03-08T07:27:58.726863Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\nfig = px.histogram(trainmeta_df, x=\"rating\", nbins=len(trainmeta_df[\"rating\"].unique()) , color_discrete_sequence=['red'])\nfig.update_layout(title_text=\"Distribution of Ratings\")\nfig.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-03-08T07:28:12.587433Z","iopub.execute_input":"2023-03-08T07:28:12.587962Z","iopub.status.idle":"2023-03-08T07:28:12.662569Z","shell.execute_reply.started":"2023-03-08T07:28:12.587895Z","shell.execute_reply":"2023-03-08T07:28:12.661151Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n

Correlation Plots

\n\n#### [Top ↑](#top)","metadata":{}},{"cell_type":"code","source":"# drop columns from correlation matrix\ncorr = trainmeta_df.corr()\n\n# create correlation heatmap\nfig = px.imshow(corr,\n labels=dict(x=\"Columns\", y=\"Columns\", color=\"Correlation\"),\n x=corr.columns,\n y=corr.columns,\n color_continuous_scale='RdBU',\n zmin=-1,\n zmax=1,\n title=\"Correlation Heatmap\")\n \n# add text annotations\nannotations = []\nfor i, row in enumerate(corr.values):\n for j, value in enumerate(row):\n text = '{:.2f}'.format(value)\n annotations.append(dict(x=corr.columns[j], y=corr.columns[i], text=text, showarrow=False))\n\nfig.update_layout(width=800, height=800)\nfig.update_traces(showscale=True, colorbar_thickness=25, colorbar_len=0.75)\nfig.update_layout(margin=dict(l=50, r=50, b=100, t=100, pad=4))\nfig.update_layout(annotations=annotations)\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-08T07:33:31.960194Z","iopub.execute_input":"2023-03-08T07:33:31.962165Z","iopub.status.idle":"2023-03-08T07:33:32.044251Z","shell.execute_reply.started":"2023-03-08T07:33:31.962092Z","shell.execute_reply":"2023-03-08T07:33:32.042974Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n

Interactive Map Plots

\n\n#### [Top ↑](#top)","metadata":{}},{"cell_type":"markdown","source":"\n

Scatter Plot

\n\n#### [Top ↑](#top)","metadata":{}},{"cell_type":"code","source":"fig = px.scatter(trainmeta_df, x=\"longitude\", y=\"latitude\", color=\"common_name\")\nfig.update_layout(title=\"Distribution of Recordings by Location\")\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-08T07:28:18.340465Z","iopub.execute_input":"2023-03-08T07:28:18.340985Z","iopub.status.idle":"2023-03-08T07:28:19.328002Z","shell.execute_reply.started":"2023-03-08T07:28:18.340938Z","shell.execute_reply":"2023-03-08T07:28:19.326674Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n

Map Box (Open Street Map)

\n\n#### [Top ↑](#top)","metadata":{}},{"cell_type":"code","source":"fig = px.scatter_mapbox(trainmeta_df, lat=\"latitude\", lon=\"longitude\", color=\"common_name\",\n hover_name=\"filename\", hover_data=[\"common_name\", \"author\", \"rating\"],\n zoom=3, height=500)\nfig.update_layout(mapbox_style=\"open-street-map\")\nfig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-08T07:30:36.116688Z","iopub.execute_input":"2023-03-08T07:30:36.118072Z","iopub.status.idle":"2023-03-08T07:30:37.787289Z","shell.execute_reply.started":"2023-03-08T07:30:36.117987Z","shell.execute_reply":"2023-03-08T07:30:37.785133Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n

Map Box(Terrain View)

\n\n#### [Top ↑](#top)","metadata":{}},{"cell_type":"code","source":"fig = px.density_mapbox(trainmeta_df, lat='latitude', lon='longitude', radius=10,\n center=dict(lat=47.6, lon=-122.3), zoom=7,\n mapbox_style=\"stamen-terrain\")\nfig.update_layout(title_text=\"Distribution of Bird Sightings\")\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-08T07:32:23.049884Z","iopub.execute_input":"2023-03-08T07:32:23.050337Z","iopub.status.idle":"2023-03-08T07:32:23.128009Z","shell.execute_reply.started":"2023-03-08T07:32:23.050299Z","shell.execute_reply":"2023-03-08T07:32:23.126718Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n

EBird Taxonomy

\n\n#### [Top ↑](#top)","metadata":{}},{"cell_type":"code","source":"train_species_df = pd.read_csv(\"/kaggle/input/birdclef-2023/eBird_Taxonomy_v2021.csv\")\ntrain_species_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-03-08T07:37:43.882566Z","iopub.execute_input":"2023-03-08T07:37:43.883006Z","iopub.status.idle":"2023-03-08T07:37:43.952049Z","shell.execute_reply.started":"2023-03-08T07:37:43.882962Z","shell.execute_reply":"2023-03-08T07:37:43.951063Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Histogram of the taxonomic order counts\nfig1 = px.histogram(train_species_df, x=\"TAXON_ORDER\", color_discrete_sequence=['aquamarine'])\nfig1.update_layout(title_text=\"Distribution of Taxonomic Orders\")\n\n# Bar plot of the species group counts\n# Box plot of the taxonomic order counts by category\nfig3 = px.box(train_species_df, x=\"CATEGORY\", y=\"TAXON_ORDER\", color_discrete_sequence=['red'])\nfig3.update_layout(title_text=\"Taxonomic Order Distribution by Category\")\n\n# Scatter plot of the taxonomic order counts by family\nfig4 = px.scatter(train_species_df, x=\"FAMILY\", y=\"TAXON_ORDER\")\nfig4.update_layout(title_text=\"Taxonomic Order Distribution by Family\")\n\n# Show the plots\nfig1.show()\nfig3.show()\nfig4.show()\n","metadata":{"execution":{"iopub.status.busy":"2023-03-08T07:38:22.185201Z","iopub.execute_input":"2023-03-08T07:38:22.185634Z","iopub.status.idle":"2023-03-08T07:38:22.501009Z","shell.execute_reply.started":"2023-03-08T07:38:22.185598Z","shell.execute_reply":"2023-03-08T07:38:22.499807Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n#

3. AUDIO EXPLORATION πŸ”‰πŸ”‰

\n\n\n#### [Top ↑](#top)","metadata":{}},{"cell_type":"markdown","source":" Audio Files : An audio file is a type of digital file format that stores recorded sound or music. It can be played back through speakers or headphones and is commonly used in a variety of applications, such as music, film, television, radio, and other forms of media. Audio files come in many different formats, including MP3, WAV, OGG, AAC, and FLAC.
\n\n\n How to Visualize Audio Files ??
\n\n There are many ways in which we can view audio in 2D like : \n\n1. Waveforms : In audio processing, a waveform is a graphical representation of a sound signal that shows how the signal varies over time. It is a plot of the amplitude of the sound wave on the y-axis versus time on the x-axis. Waveforms can be used to visualize and analyze the properties of audio signals, such as frequency, amplitude, phase, and duration.
\n\n\n2. Spectograms : A spectrogram is a visual representation of the spectrum of frequencies of a sound or other signal as it varies with time. It is a way of analyzing the sound signal to understand how the signal changes over time and what frequencies are present in the signal at any given point in time. The x-axis of a spectrogram represents time, while the y-axis represents frequency. The intensity of the color or brightness of each point in the spectrogram represents the amplitude or energy of the frequency at that point in time.
\n\n3. Mel Spectograms : Mel spectrograms are a type of spectrogram that use the Mel scale to determine the spacing of frequency bins. The Mel scale is a perceptual scale of pitches judged by listeners to be equal in distance from one another. By using the Mel scale, the frequency bins are spaced closer together at lower frequencies and farther apart at higher frequencies, which better matches the way humans perceive sound.
\n\n4. Chromagram : A chromagram is a visual representation of the pitch content of an audio signal, where each column corresponds to a particular frequency band or pitch class. It is a 2D representation of the distribution of energy in a sound, plotted as a function of time and pitch class.The chromagram is calculated by taking the short-time Fourier transform (STFT) of the audio signal and projecting the resulting spectrogram onto a pitch-class basis, typically using a bank of 12 log-spaced filters to represent the 12 pitch classes of the Western musical scale
\n\n\n5. MFCC : MFCC stands for Mel Frequency Cepstral Coefficients, which is a feature extraction technique commonly used in speech processing, music information retrieval, and other related fields.MFCCs are derived from a short-time Fourier transform (STFT) of the audio signal, which involves breaking the signal down into small, overlapping segments and performing a Fourier transform on each segment to obtain its frequency spectrum. The Mel scale is then applied to the frequency spectrum to transform it into a logarithmic scale that approximates the human auditory system's perception of sound.
\n","metadata":{}},{"cell_type":"markdown","source":"\n

Helper Function

\n\n#### [Top ↑](#top)","metadata":{}},{"cell_type":"code","source":"def audio_eda(audio_path):\n \n # Load an audio file\n samples, sample_rate = librosa.load(audio_path)\n\n # Visualize the waveform\n plt.figure(figsize=(14, 5))\n librosa.display.waveshow(samples, sr=sample_rate)\n plt.title('Waveform')\n\n # Compute the spectrogram\n spectrogram = librosa.stft(samples)\n spectrogram_db = librosa.amplitude_to_db(abs(spectrogram))\n\n # Visualize the spectrogram\n plt.figure(figsize=(14, 5))\n librosa.display.specshow(spectrogram_db, sr=sample_rate, x_axis='time', y_axis='log')\n plt.colorbar(format='%+2.0f dB')\n plt.title('Spectrogram (dB)')\n\n # Compute the mel spectrogram\n\n\n # Visualize the mel spectrogram\n S = librosa.feature.melspectrogram(y=samples, sr=sample_rate)\n\n # Visualize mel spectrogram\n plt.figure(figsize=(10, 4))\n librosa.display.specshow(librosa.power_to_db(S, ref=np.max), y_axis='mel', fmax=8000, x_axis='time')\n plt.colorbar(format='%+2.0f dB')\n plt.title('Mel spectrogram')\n plt.tight_layout()\n\n\n # Compute the chromagram\n chromagram = librosa.feature.chroma_stft( y = samples , sr = sample_rate)\n\n # Visualize the chromagram\n plt.figure(figsize=(14, 5))\n librosa.display.specshow(chromagram, sr=sample_rate, x_axis='time', y_axis='chroma')\n plt.colorbar()\n plt.title('Chromagram')\n\n # Compute the MFCCs\n mfccs = librosa.feature.mfcc(y=samples, sr=sample_rate, n_mfcc=13)\n\n # Visualize the MFCCs\n plt.figure(figsize=(14, 5))\n librosa.display.specshow(mfccs, sr=sample_rate, x_axis='time')\n plt.colorbar()\n plt.title('MFCCs')\n\n # Show the plots\n display(Audio(samples, rate=sample_rate))\n plt.show()","metadata":{"_kg_hide-input":false,"execution":{"iopub.status.busy":"2023-03-08T08:02:19.274177Z","iopub.execute_input":"2023-03-08T08:02:19.275850Z","iopub.status.idle":"2023-03-08T08:02:19.289095Z","shell.execute_reply.started":"2023-03-08T08:02:19.275797Z","shell.execute_reply":"2023-03-08T08:02:19.287815Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n

Audio Exploration

\n\n#### [Top ↑](#top)","metadata":{}},{"cell_type":"markdown","source":"\n

Black-and-white Mannikin

\n\n\n Primary Label : bawman1
\n\n Scientific Name : Spermestes bicolor
\n\n#### [Top ↑](#top)\n","metadata":{}},{"cell_type":"code","source":"audio_eda(\"/kaggle/input/birdclef-2023/train_audio/bawman1/XC115075.ogg\")","metadata":{"execution":{"iopub.status.busy":"2023-03-08T08:02:20.350192Z","iopub.execute_input":"2023-03-08T08:02:20.350697Z","iopub.status.idle":"2023-03-08T08:02:25.664783Z","shell.execute_reply.started":"2023-03-08T08:02:20.350629Z","shell.execute_reply":"2023-03-08T08:02:25.663507Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n

African Black-headed Oriole

\n\n\n Primary Label : abhori1
\n\n Scientific Name : Oriolus larvatus
\n\n#### [Top ↑](#top)\n\n\n","metadata":{}},{"cell_type":"code","source":"audio_eda(\"/kaggle/input/birdclef-2023/train_audio/abhori1/XC120251.ogg\")","metadata":{"execution":{"iopub.status.busy":"2023-03-08T08:14:52.241546Z","iopub.execute_input":"2023-03-08T08:14:52.242091Z","iopub.status.idle":"2023-03-08T08:14:57.766108Z","shell.execute_reply.started":"2023-03-08T08:14:52.242048Z","shell.execute_reply":"2023-03-08T08:14:57.764896Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n

African Bare-eyed Thrush

\n\n\n Primary Label : abethr1
\n\n Scientific Name : Turdus tephronotus
\n\n#### [Top ↑](#top)\n","metadata":{}},{"cell_type":"code","source":"audio_eda(\"/kaggle/input/birdclef-2023/train_audio/abethr1/XC128013.ogg\")","metadata":{"execution":{"iopub.status.busy":"2023-03-08T08:17:17.536886Z","iopub.execute_input":"2023-03-08T08:17:17.537364Z","iopub.status.idle":"2023-03-08T08:17:21.542035Z","shell.execute_reply.started":"2023-03-08T08:17:17.537326Z","shell.execute_reply":"2023-03-08T08:17:21.540716Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n

African Gray Flycatcher

\n\n\n Primary Label : afgfly1
\n\n Scientific Name : Bradornis microrhynchus
\n\n#### [Top ↑](#top)\n","metadata":{}},{"cell_type":"code","source":"audio_eda(\"/kaggle/input/birdclef-2023/train_audio/afgfly1/XC134487.ogg\")","metadata":{"execution":{"iopub.status.busy":"2023-03-08T08:21:00.509819Z","iopub.execute_input":"2023-03-08T08:21:00.510226Z","iopub.status.idle":"2023-03-08T08:21:03.054197Z","shell.execute_reply.started":"2023-03-08T08:21:00.510192Z","shell.execute_reply":"2023-03-08T08:21:03.052744Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n

African Goshawk

\n\n\n Primary Label : afrgos1
\n\n Scientific Name : Accipiter tachiro
\n\n#### [Top ↑](#top)\n","metadata":{}},{"cell_type":"code","source":"audio_eda(\"/kaggle/input/birdclef-2023/train_audio/afrgos1/XC115984.ogg\")","metadata":{"execution":{"iopub.status.busy":"2023-03-08T08:26:28.777924Z","iopub.execute_input":"2023-03-08T08:26:28.778364Z","iopub.status.idle":"2023-03-08T08:26:32.453977Z","shell.execute_reply.started":"2023-03-08T08:26:28.778321Z","shell.execute_reply":"2023-03-08T08:26:32.452728Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n
\nThank You for reading 😊
\nPlease do upvote if you liked the notebook
\nIf you have any suggestions or feeback, please let me know
\n
","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}