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| #!/usr/bin/env python | |
| # coding: utf-8 | |
| # In[ ]: | |
| import librosa | |
| import librosa.display | |
| import numpy as np | |
| import pandas as pd | |
| import scipy | |
| from scipy.stats import skew | |
| import matplotlib.pyplot as plt | |
| SAMPLE_RATE = 44100 | |
| def get_mfcc(name, path): | |
| data, _ = librosa.core.load(path + name, sr = SAMPLE_RATE) | |
| assert _ == SAMPLE_RATE | |
| try: | |
| ft1 = librosa.feature.mfcc(data, sr = SAMPLE_RATE, n_mfcc=30) | |
| ft2 = librosa.feature.zero_crossing_rate(data)[0] | |
| ft3 = librosa.feature.spectral_rolloff(data)[0] | |
| ft4 = librosa.feature.spectral_centroid(data)[0] | |
| ft5 = librosa.feature.spectral_contrast(data)[0] | |
| ft6 = librosa.feature.spectral_bandwidth(data)[0] | |
| ft1_trunc = np.hstack((np.mean(ft1, axis=1), np.std(ft1, axis=1), skew(ft1, axis = 1), np.max(ft1, axis = 1), | |
| np.median(ft1, axis = 1), np.min(ft1, axis = 1))) | |
| ft2_trunc = np.hstack((np.mean(ft2), np.std(ft2), skew(ft2), np.max(ft2), np.median(ft2), np.min(ft2))) | |
| ft3_trunc = np.hstack((np.mean(ft3), np.std(ft3), skew(ft3), np.max(ft3), np.median(ft3), np.min(ft3))) | |
| ft4_trunc = np.hstack((np.mean(ft4), np.std(ft4), skew(ft4), np.max(ft4), np.median(ft4), np.min(ft4))) | |
| ft5_trunc = np.hstack((np.mean(ft5), np.std(ft5), skew(ft5), np.max(ft5), np.median(ft5), np.min(ft5))) | |
| ft6_trunc = np.hstack((np.mean(ft6), np.std(ft6), skew(ft6), np.max(ft6), np.median(ft6), np.max(ft6))) | |
| return pd.Series(np.hstack((ft1_trunc, ft2_trunc, ft3_trunc, ft4_trunc, ft5_trunc, ft6_trunc))) | |
| except: | |
| print('bad file') | |
| return pd.Series([0]*210) | |
| def MFCC_spectrogram(y, sr): | |
| # 提取頻域音頻資料 MFCC spectrogram feature | |
| melspec = librosa.feature.melspectrogram(y, sr, n_fft=1024, hop_length=512, n_mels=128) | |
| # 轉成 log scale 已呈現頻譜資料 | |
| logmelspec = librosa.power_to_db(melspec) | |
| # 繪圖 | |
| plt.figure() | |
| librosa.display.specshow(logmelspec, sr=sr, x_axis='time', y_axis='mel') | |
| plt.title('Features of unknow frog by MFCC (Mel Frquency Cepstral Coefficients)') | |
| plt.show() | |
| def FeatureExtraction(fname): | |
| # 創建文件檔 | |
| test= pd.DataFrame({'fname':[fname]}) | |
| # 特徵提取 | |
| test_data = test['fname'].progress_apply(get_mfcc, path='') | |
| test_data = test_data.values | |
| return test_data | |