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license: mit
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## 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.

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### **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` |

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### **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

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### **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.”**

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### **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)