AIOmarRehan/General_Audio_Dataset

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.”