Update README.md

README.md

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-license: unknown
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+---
+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:
+
+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
+
+---
+
+### **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.”**