{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\n# for dirname, _, filenames in os.walk('/kaggle/input'):\n#     for filename in filenames:\n#         print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import librosa, librosa.display\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport IPython.display as ipd","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pd.set_option(\"display.max_columns\", 40)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df = pd.read_csv(\"/kaggle/input/birdsong-recognition/train.csv\")\ntrain_df.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"base_dir = \"/kaggle/input/birdsong-recognition/train_audio/\"\ntrain_df['full_path'] = base_dir + train_df['ebird_code'] + '/' + train_df['filename']","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# ***Aldfly Bird***","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"# Lets see about the 'aldfly' bird\nfile = train_df[train_df['ebird_code'] == 'aldfly'].reset_index()['full_path'][0]\nsignal, sr = librosa.load(file, sr=40200)\n# WAVEPLOT\nlibrosa.display.waveplot(signal, sr = sr)\nplt.xlabel(\"Time\")\nplt.ylabel(\"Amplitude\")\nplt.show()\n\n\nn_fft = 2048\nhop_length = 512 # How much we have to shift\nstft = librosa.core.stft(signal, hop_length = hop_length, n_fft = n_fft)\nspectrogram = np.abs(stft)\n\nlog_spectrogram  = librosa.amplitude_to_db(spectrogram)\n\n# SPECTROFRAM\nlibrosa.display.specshow(spectrogram, sr = sr, hop_length = hop_length)\nplt.xlabel(\"Time\")\nplt.ylabel(\"Frequency\")\nplt.colorbar()\nplt.set_cmap(\"viridis\")\nplt.show()\n\n# LOG SPECTROGRAM\nlibrosa.display.specshow(log_spectrogram, sr = sr, hop_length = hop_length)\nplt.xlabel(\"Time\")\nplt.ylabel(\"Frequency\")\nplt.set_cmap(\"viridis\")\nplt.colorbar()\nplt.show()\n\n#MFCCs\nMFCCs = librosa.feature.mfcc(signal, n_fft = n_fft, hop_length = hop_length, n_mfcc = 13)\nlibrosa.display.specshow(MFCCs, sr = sr, hop_length = hop_length)\nplt.xlabel(\"Time\")\nplt.ylabel(\"MFCCs\")\nplt.set_cmap(\"viridis\")\nplt.colorbar()\nplt.show()\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ipd.Audio(file)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# ***Ameavo Bird***","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"# Lets see about the 'ameavo' bird\nfile = train_df[train_df['ebird_code'] == 'ameavo'].reset_index()['full_path'][0]\nsignal, sr = librosa.load(file, sr=40200)\n# WAVEPLOT\nlibrosa.display.waveplot(signal, sr = sr)\nplt.xlabel(\"Time\")\nplt.ylabel(\"Amplitude\")\nplt.show()\n\n\nn_fft = 2048\nhop_length = 512 # How much we have to shift\nstft = librosa.core.stft(signal, hop_length = hop_length, n_fft = n_fft)\nspectrogram = np.abs(stft)\n\nlog_spectrogram  = librosa.amplitude_to_db(spectrogram)\n\n# SPECTROFRAM\nlibrosa.display.specshow(spectrogram, sr = sr, hop_length = hop_length)\nplt.xlabel(\"Time\")\nplt.ylabel(\"Frequency\")\nplt.colorbar()\nplt.set_cmap(\"viridis\")\nplt.show()\n\n# LOG SPECTROGRAM\nlibrosa.display.specshow(log_spectrogram, sr = sr, hop_length = hop_length)\nplt.xlabel(\"Time\")\nplt.ylabel(\"Frequency\")\nplt.set_cmap(\"viridis\")\nplt.colorbar()\nplt.show()\n\n#MFCCs\nMFCCs = librosa.feature.mfcc(signal, n_fft = n_fft, hop_length = hop_length, n_mfcc = 13)\nlibrosa.display.specshow(MFCCs, sr = sr, hop_length = hop_length)\nplt.xlabel(\"Time\")\nplt.ylabel(\"MFCCs\")\nplt.set_cmap(\"viridis\")\nplt.colorbar()\nplt.show()\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ipd.Audio(file)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# ***Brdowl Bird***","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"# Lets see about the 'brdowl' bird\nfile = train_df[train_df['ebird_code'] == 'brdowl'].reset_index()['full_path'][0]\nsignal, sr = librosa.load(file, sr=40200)\n# WAVEPLOT\nlibrosa.display.waveplot(signal, sr = sr)\nplt.xlabel(\"Time\")\nplt.ylabel(\"Amplitude\")\nplt.show()\n\n\nn_fft = 2048\nhop_length = 512 # How much we have to shift\nstft = librosa.core.stft(signal, hop_length = hop_length, n_fft = n_fft)\nspectrogram = np.abs(stft)\n\nlog_spectrogram  = librosa.amplitude_to_db(spectrogram)\n\n# SPECTROFRAM\nlibrosa.display.specshow(spectrogram, sr = sr, hop_length = hop_length)\nplt.xlabel(\"Time\")\nplt.ylabel(\"Frequency\")\nplt.colorbar()\nplt.set_cmap(\"viridis\")\nplt.show()\n\n# LOG SPECTROGRAM\nlibrosa.display.specshow(log_spectrogram, sr = sr, hop_length = hop_length)\nplt.xlabel(\"Time\")\nplt.ylabel(\"Frequency\")\nplt.set_cmap(\"viridis\")\nplt.colorbar()\nplt.show()\n\n#MFCCs\nMFCCs = librosa.feature.mfcc(signal, n_fft = n_fft, hop_length = hop_length, n_mfcc = 13)\nlibrosa.display.specshow(MFCCs, sr = sr, hop_length = hop_length)\nplt.xlabel(\"Time\")\nplt.ylabel(\"MFCCs\")\nplt.set_cmap(\"viridis\")\nplt.colorbar()\nplt.show()\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ipd.Audio(file)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","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}