| import numpy as np | |
| import librosa | |
| def extract_features(audio, sr): | |
| features = {} | |
| pitches, magnitudes = librosa.piptrack(y=audio, sr=sr) | |
| pitch_values = pitches[pitches > 0] | |
| features["pitch_mean"] = float(np.mean(pitch_values)) if len(pitch_values) > 0 else 0.0 | |
| features["pitch_std"] = float(np.std(pitch_values)) if len(pitch_values) > 0 else 0.0 | |
| mfcc = librosa.feature.mfcc(y=audio, sr=sr, n_mfcc=13) | |
| mfcc_means = np.mean(mfcc, axis=1) | |
| for i, val in enumerate(mfcc_means): | |
| features[f"mfcc_{i+1}"] = float(val) | |
| centroid = librosa.feature.spectral_centroid(y=audio, sr=sr) | |
| features["spectral_centroid_mean"] = float(np.mean(centroid)) | |
| rms = librosa.feature.rms(y=audio) | |
| features["rms_std"] = float(np.std(rms)) | |
| zcr = librosa.feature.zero_crossing_rate(y=audio) | |
| features["zcr_mean"] = float(np.mean(zcr)) | |
| return features | |