File size: 1,445 Bytes
de9c0fe b6c1b75 de9c0fe | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | 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) # simple RMS target ≈ loudness norm
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]])
|