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Audio preprocessing and feature extraction module for respiratory disease detection.
"""
import librosa
import numpy as np
import soundfile as sf
from pathlib import Path
from typing import Tuple, Dict, Optional
import warnings
warnings.filterwarnings('ignore')
class AudioPreprocessor:
"""Handles audio loading, normalization, and feature extraction."""
def __init__(self, sample_rate: int = 16000, duration: float = 5.0):
"""
Initialize the audio preprocessor.
Args:
sample_rate: Target sample rate for all audio files
duration: Target duration in seconds (will pad/trim)
"""
self.sample_rate = sample_rate
self.duration = duration
self.target_length = int(sample_rate * duration)
def load_audio(self, file_path: str) -> np.ndarray:
"""
Load and normalize audio file.
Args:
file_path: Path to audio file
Returns:
Normalized audio array
"""
try:
# Load audio file
audio, sr = librosa.load(file_path, sr=self.sample_rate, mono=True)
# Normalize audio to fixed length
audio = self._normalize_length(audio)
# Normalize amplitude
audio = librosa.util.normalize(audio)
return audio
except Exception as e:
print(f"Error loading {file_path}: {e}")
return np.zeros(self.target_length)
def _normalize_length(self, audio: np.ndarray) -> np.ndarray:
"""Pad or trim audio to target length."""
if len(audio) < self.target_length:
# Pad with zeros
audio = np.pad(audio, (0, self.target_length - len(audio)))
else:
# Trim to target length
audio = audio[:self.target_length]
return audio
def extract_mfcc(self, audio: np.ndarray, n_mfcc: int = 40) -> np.ndarray:
"""
Extract MFCC features from audio.
Args:
audio: Audio signal
n_mfcc: Number of MFCCs to extract
Returns:
MFCC features (n_mfcc, time_steps)
"""
mfcc = librosa.feature.mfcc(
y=audio,
sr=self.sample_rate,
n_mfcc=n_mfcc,
n_fft=2048,
hop_length=512
)
return mfcc
def extract_mel_spectrogram(self, audio: np.ndarray, n_mels: int = 128) -> np.ndarray:
"""
Extract mel spectrogram from audio.
Args:
audio: Audio signal
n_mels: Number of mel bands
Returns:
Mel spectrogram
"""
mel_spec = librosa.feature.melspectrogram(
y=audio,
sr=self.sample_rate,
n_mels=n_mels,
n_fft=2048,
hop_length=512
)
# Convert to log scale
mel_spec_db = librosa.power_to_db(mel_spec, ref=np.max)
return mel_spec_db
def extract_spectral_features(self, audio: np.ndarray) -> Dict[str, np.ndarray]:
"""
Extract various spectral features.
Args:
audio: Audio signal
Returns:
Dictionary of spectral features
"""
features = {}
# Spectral centroid
features['spectral_centroid'] = librosa.feature.spectral_centroid(
y=audio, sr=self.sample_rate
)[0]
# Spectral rolloff
features['spectral_rolloff'] = librosa.feature.spectral_rolloff(
y=audio, sr=self.sample_rate
)[0]
# Zero crossing rate
features['zero_crossing_rate'] = librosa.feature.zero_crossing_rate(audio)[0]
# Chroma features
features['chroma'] = librosa.feature.chroma_stft(
y=audio, sr=self.sample_rate
)
# Spectral contrast
features['spectral_contrast'] = librosa.feature.spectral_contrast(
y=audio, sr=self.sample_rate
)
return features
def extract_all_features(self, audio: np.ndarray) -> Dict[str, np.ndarray]:
"""
Extract all audio features.
Args:
audio: Audio signal
Returns:
Dictionary containing all features
"""
features = {
'mfcc': self.extract_mfcc(audio),
'mel_spectrogram': self.extract_mel_spectrogram(audio),
}
features.update(self.extract_spectral_features(audio))
return features
def compute_statistics(self, feature_array: np.ndarray) -> np.ndarray:
"""
Compute statistical features (mean, std, min, max) from feature array.
Args:
feature_array: 2D feature array (features, time)
Returns:
Flattened statistical features
"""
stats = []
stats.extend(np.mean(feature_array, axis=1))
stats.extend(np.std(feature_array, axis=1))
stats.extend(np.min(feature_array, axis=1))
stats.extend(np.max(feature_array, axis=1))
return np.array(stats)
class AudioAugmenter:
"""Augments audio data for better model generalization."""
@staticmethod
def add_noise(audio: np.ndarray, noise_level: float = 0.005) -> np.ndarray:
"""Add random noise to audio."""
noise = np.random.randn(len(audio))
return audio + noise_level * noise
@staticmethod
def time_stretch(audio: np.ndarray, rate: float = 1.2) -> np.ndarray:
"""Time stretch audio."""
return librosa.effects.time_stretch(audio, rate=rate)
@staticmethod
def pitch_shift(audio: np.ndarray, sr: int, n_steps: int = 2) -> np.ndarray:
"""Shift pitch of audio."""
return librosa.effects.pitch_shift(audio, sr=sr, n_steps=n_steps)
@staticmethod
def random_gain(audio: np.ndarray, min_gain: float = 0.8, max_gain: float = 1.2) -> np.ndarray:
"""Apply random gain to audio."""
gain = np.random.uniform(min_gain, max_gain)
return audio * gain
def augment(self, audio: np.ndarray, sr: int, techniques: list = None) -> np.ndarray:
"""
Apply random augmentation techniques.
Args:
audio: Audio signal
sr: Sample rate
techniques: List of augmentation techniques to apply
Returns:
Augmented audio
"""
if techniques is None:
techniques = ['noise', 'gain']
augmented = audio.copy()
for technique in techniques:
if technique == 'noise' and np.random.rand() > 0.5:
augmented = self.add_noise(augmented)
elif technique == 'pitch' and np.random.rand() > 0.5:
n_steps = np.random.randint(-2, 3)
augmented = self.pitch_shift(augmented, sr, n_steps)
elif technique == 'stretch' and np.random.rand() > 0.5:
rate = np.random.uniform(0.9, 1.1)
augmented = self.time_stretch(augmented, rate)
elif technique == 'gain' and np.random.rand() > 0.5:
augmented = self.random_gain(augmented)
return augmented
def process_dataset(data_dir: str, output_dir: str, preprocessor: AudioPreprocessor):
"""
Process all audio files in a dataset directory.
Args:
data_dir: Directory containing raw audio files
output_dir: Directory to save processed features
preprocessor: AudioPreprocessor instance
"""
data_path = Path(data_dir)
output_path = Path(output_dir)
output_path.mkdir(parents=True, exist_ok=True)
audio_files = list(data_path.rglob('*.wav')) + list(data_path.rglob('*.mp3'))
print(f"Found {len(audio_files)} audio files")
for audio_file in audio_files:
try:
# Load and preprocess audio
audio = preprocessor.load_audio(str(audio_file))
# Extract features
features = preprocessor.extract_all_features(audio)
# Save features
relative_path = audio_file.relative_to(data_path)
output_file = output_path / relative_path.with_suffix('.npz')
output_file.parent.mkdir(parents=True, exist_ok=True)
np.savez_compressed(output_file, **features)
except Exception as e:
print(f"Error processing {audio_file}: {e}")
print(f"Processing complete. Features saved to {output_dir}")
if __name__ == "__main__":
# Example usage
preprocessor = AudioPreprocessor(sample_rate=16000, duration=5.0)
# Process a single file (example)
# audio = preprocessor.load_audio("path/to/audio.wav")
# features = preprocessor.extract_all_features(audio)
# print("MFCC shape:", features['mfcc'].shape)
# print("Mel spectrogram shape:", features['mel_spectrogram'].shape)
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