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license: unknown |
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## Audio Dataset |
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> **This raw audio dataset was prepared using my notebook |
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> *“Building an Audio Classification Pipeline with DL,”* available on my profile.** |
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> It forms the foundation for all subsequent preprocessing and spectrogram generation. |
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### **Dataset Summary** |
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| Property | Description | |
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| ------------------------- | ----------------------- | |
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| **Number of Classes** | 13 categories | |
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| **Audio Files per Class** | ~40 raw recordings | |
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| **Duration** | ~5 seconds each | |
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| **Channels** | Mono (after processing) | |
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| **Sampling Rate (final)** | 16 kHz | |
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### **Processing Overview** |
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The raw audio underwent a compact but essential pipeline: |
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1. **Data Loading & Inspection** |
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Imported all recordings and validated metadata (duration, sample rate, SNR). |
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2. **Cleaning & Normalization** |
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* Removed corrupted/silent files |
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* Normalized amplitude |
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* Trimmed leading/trailing silence |
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* Applied noise reduction |
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3. **Standardization** |
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* Converted to mono |
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* Resampled to **16,000 Hz** |
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* Forced each clip to a **uniform 5-second length** |
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4. **Augmentation (for balance & variability)** |
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* Pitch shift |
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* Time stretch |
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* Noise injection |
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* Time shift |
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### **Final Technical Description** |
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> **“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.”** |
