license: unknown
Audio Dataset
> This raw audio dataset was prepared using my notebook > “Building an Audio Classification Pipeline with DL,” available on my profile. > It forms the foundation for all subsequent preprocessing and spectrogram generation.
Dataset Summary
| Property | Description |
|---|---|
| Number of Classes | 13 categories |
| Audio Files per Class | ~40 raw recordings |
| Duration | ~5 seconds each |
| Channels | Mono (after processing) |
| Sampling Rate (final) | 16 kHz |
Processing Overview
The raw audio underwent a compact but essential pipeline:
Data Loading & Inspection Imported all recordings and validated metadata (duration, sample rate, SNR).
Cleaning & Normalization
- Removed corrupted/silent files
- Normalized amplitude
- Trimmed leading/trailing silence
- Applied noise reduction
Standardization
- Converted to mono
- Resampled to 16,000 Hz
- Forced each clip to a uniform 5-second length
Augmentation (for balance & variability)
- Pitch shift
- Time stretch
- Noise injection
- Time shift
Final Technical Description
> “The raw dataset consists of 13 audio classes with approximately 40 five-second recordings each. All clips were cleaned, normalized, noise-reduced, resampled, and standardized through a custom pipeline implemented in the notebook ‘Building an Audio Classification Pipeline with DL.’ This processed audio served as the basis for generating the Mel-spectrogram dataset used for model training.”