AIOmarRehan/Mel_Spectrogram_Images_for_Audio_Classification

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

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

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