| """ |
| =============================================================================== |
| preprocessing/__init__.py — Audio Preprocessing Pipeline |
| =============================================================================== |
| |
| This package contains all audio-level preprocessing steps that happen BEFORE |
| feature extraction (mel spectrograms). The pipeline order is: |
| |
| Raw WAV → Resampling (JSON) → Normalization (JSON) |
| → Noise Reduction (EL sir) → Silence Removal (EL sir) |
| → Clean numpy array ready for feature extraction (sala7) |
| |
| OWNERS: EL sir (noise_reduction, silence_removal) |
| JSON (normalization, resampling) |
| =============================================================================== |
| """ |
|
|
| from preprocessing.resampling import resample_audio |
| from preprocessing.normalization import normalize_volume |
| from preprocessing.noise_reduction import reduce_noise |
| from preprocessing.silence_removal import remove_silence |
|
|
|
|
| def preprocess_audio(file_path): |
| """ |
| Full preprocessing pipeline: load → resample → normalize → denoise → trim. |
| |
| This is the main entry point that JSON's Dataset class and infer.py will call. |
| It chains all four preprocessing steps in the correct order and returns |
| a clean numpy array ready for mel spectrogram extraction. |
| |
| Parameters |
| ---------- |
| file_path : str |
| Absolute path to the .wav file. |
| |
| Returns |
| ------- |
| audio : np.ndarray |
| 1D numpy array of the preprocessed audio waveform. |
| sr : int |
| Sampling rate (always TARGET_SR after resampling). |
| |
| Pipeline Order & Rationale |
| -------------------------- |
| 1. Resample FIRST — all downstream steps assume a fixed sample rate. |
| If we denoise before resampling, the noise profile frequency bins |
| won't match after resampling. |
| 2. Normalize SECOND — bring all recordings to the same loudness level |
| before noise estimation, so the noise threshold is consistent. |
| 3. Denoise THIRD — remove background hum/motor noise. |
| 4. Trim LAST — now that noise is gone, energy-based silence detection |
| can accurately find where the real machine sound starts/ends. |
| """ |
| |
| audio, sr = resample_audio(file_path) |
|
|
| |
| audio = normalize_volume(audio) |
|
|
| |
| audio = reduce_noise(audio, sr) |
|
|
| |
| audio = remove_silence(audio, sr) |
|
|
| return audio, sr |
|
|
