Audio Processing Pipeline
Our system implements a sophisticated audio processing pipeline to prepare audio files for deep learning analysis.
Processing Parameters
Sample Rate
16000 Hz
Standard sampling rate for speech processingDuration
5 seconds
Fixed length for consistent processingMel Bins
128 bins
Frequency resolution for spectrogramsTime Steps
109 steps
Fixed width for model inputProcessing Steps
1
Audio Loading
Audio files are loaded using librosa.load with specified sample rate and duration
- Sample rate: 16000 Hz
- Duration: 5 seconds
- Format: FLAC files
2
Spectrogram Generation
Mel spectrograms are extracted using librosa.feature.melspectrogram
- 128 Mel frequency bins
- Power spectrogram conversion
- Log-mel spectrogram computation
3
Normalization
Spectrograms are converted to decibels and normalized
- Power to decibel conversion
- Amplitude normalization
- Dynamic range compression
4
Padding/Truncation
All spectrograms are standardized to MAX_TIME_STEPS
- Zero padding for shorter sequences
- Truncation for longer sequences
- Fixed width: 109 time steps
Feature Extraction
The system extracts several key features from the audio signals:
- Mel-frequency cepstral coefficients (MFCCs)
- Spectral contrast
- Chroma features
- Temporal features