| import numpy as np, librosa |
|
|
|
|
| def load_mono(path, sr=16000): |
| path = str(path) |
| x, sr = librosa.load(path, sr=sr, mono=True) |
| x, _ = librosa.effects.trim(x, top_db=30) |
| rms = np.sqrt(np.mean(x**2)) + 1e-8 |
| x = x * (0.05 / rms) |
| return x, sr |
|
|
|
|
| def extract_features(x, sr=16000, n_mels=64, n_mfcc=20): |
| S = librosa.feature.melspectrogram(y=x, sr=sr, n_mels=n_mels) |
| logmel = librosa.power_to_db(S + 1e-9) |
| logmel_stats = np.hstack([logmel.mean(axis=1), logmel.std(axis=1)]) |
|
|
| mfcc = librosa.feature.mfcc(S=librosa.power_to_db(S + 1e-9), sr=sr, n_mfcc=n_mfcc) |
| frames = mfcc.shape[1] |
| width = min(9, frames if frames % 2 else frames - 1) |
| if width < 3: |
| d1 = np.zeros_like(mfcc) |
| d2 = np.zeros_like(mfcc) |
| else: |
| d1 = librosa.feature.delta(mfcc, width=width) |
| d2 = librosa.feature.delta(mfcc, width=width, order=2) |
| mfcc_stats = np.hstack([mfcc.mean(axis=1), mfcc.std(axis=1), |
| d1.mean(axis=1), d1.std(axis=1), |
| d2.mean(axis=1), d2.std(axis=1)]) |
|
|
| zcr = librosa.feature.zero_crossing_rate(x).mean() |
| centroid = librosa.feature.spectral_centroid(y=x, sr=sr).mean() |
| rolloff = librosa.feature.spectral_rolloff(y=x, sr=sr).mean() |
| flatness = librosa.feature.spectral_flatness(y=x).mean() |
| return np.hstack([logmel_stats, mfcc_stats, [zcr, centroid, rolloff, flatness]]) |
|
|
