Update README.md

README.md

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-license: mit
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+---
+license: mit
+---
+
+## Mel-Spectrogram Image Dataset (Generated via Custom Pipeline)
+
+> **This dataset was fully generated through my notebook
+> *“Building an Audio Classification Pipeline with DL”* available on my profile.**
+> It represents a complete end-to-end transformation from raw audio to clean, balanced Mel-spectrogram images suitable for deep learning.
+
+---
+
+### **Dataset Summary**
+
+| Property                     | Description                                   |
+| ---------------------------- | --------------------------------------------- |
+| **Number of Classes**        | 13 distinct audio categories                  |
+| **Original Audio per Class** | ~40 raw recordings                            |
+| **Average Duration**         | ~5 seconds per audio file                     |
+| **Final Images per Class**   | 125 Mel-spectrogram images                    |
+| **Final Dataset Size**       | 13 × 125 = **1625 images**                    |
+| **Sampling Rate**            | Standardized to **16 kHz**                    |
+| **Audio Length**             | Uniform **5-second** fixed length             |
+| **Spectrogram Type**         | 128-Mel frequency bins, `melspectrogram → dB` |
+
+---
+
+### **High-Level Processing Pipeline**
+
+The dataset was built using a **fully custom preprocessing, cleaning, and augmentation pipeline**, implemented step-by-step in the notebook.
+
+#### **1. Data Ingestion**
+
+* Loaded all raw audio files from 13 folders
+* Parsed metadata (sample rate, duration, amplitude, SNR, etc.)
+
+#### **2. Cleaning & Standardization**
+
+* Removed corrupt, silent, or unreadable audio files
+* Normalized peak amplitudes
+* Trimmed silence using `librosa.effects.trim`
+* Performed noise reduction (`noisereduce`)
+* Converted all audio to **mono**
+* Resampled to **16,000 Hz**
+* Ensured each sample is **exactly 5 seconds**
+
+#### **3. Dataset Balancing**
+
+* Used augmentation for minority classes
+* Used controlled undersampling or oversampling where necessary
+* Verified all classes contain equal counts
+
+#### **4. Audio Augmentation (Used for Balancing & Variability)**
+
+Augmentations built with **audiomentations**:
+
+* Time shift
+* Pitch shift
+* Time stretching
+* Gaussian noise injection
+* Random perturbations for robustness
+
+#### **5. Splitting & Chunking**
+
+* Long samples were split into 5-second chunks
+* Shorter samples padded to match target duration
+* Ensured strict uniformity before feature extraction
+
+#### **6. Mel-Spectrogram Generation**
+
+Converted all cleaned audio files into Mel-spectrogram images using:
+
+* `n_fft = 1024`
+* `hop_length = 512`
+* `n_mels = 128`
+* Converted to decibel scale (`power_to_db`)
+* Saved images in **RGBA format** to preserve color-mapped spectral information
+
+---
+
+### **Final Technical Description**
+
+> **“The final dataset consists of 13 audio classes, each expanded to exactly 125 Mel-spectrogram images through a rigorous pipeline of cleaning, normalization, augmentation, noise reduction, resampling, duration standardization, and feature extraction. All processing steps were implemented in my notebook *‘Building an Audio Classification Pipeline with DL,’* where raw 5-second audio recordings were transformed into high-quality Mel-spectrogram images suitable for deep learning models.”**
+
+---
+
+### **Examples of the Images**
+
+![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F27304693%2Ffdf7046a261734cd8f503c8f448ca6ad%2Fdownload.png?generation=1763570826533634&alt=media)
+
+![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F27304693%2Fea53570ce051601192c90770091f7ceb%2Fdownload%20(1).png?generation=1763570855911665&alt=media)