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license: unknown
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## 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.
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### **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 |
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### **Processing Overview**
The raw audio underwent a compact but essential pipeline:
1. **Data Loading & Inspection**
Imported all recordings and validated metadata (duration, sample rate, SNR).
2. **Cleaning & Normalization**
* Removed corrupted/silent files
* Normalized amplitude
* Trimmed leading/trailing silence
* Applied noise reduction
3. **Standardization**
* Converted to mono
* Resampled to **16,000 Hz**
* Forced each clip to a **uniform 5-second length**
4. **Augmentation (for balance & variability)**
* Pitch shift
* Time stretch
* Noise injection
* Time shift
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### **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.”** |