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. 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… See the full description on the dataset page: https://huggingface.co/datasets/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.
Dataset Summary
Processing Overview
The raw audio underwent a compact but essential pipeline:
- Data Loading & Inspection Imported all recordings and validated metadata (duration, sample rate, SNR).
- Cleaning & Normalization
- Removed corrupted/silent files
- Normalized amplitude
- Trimmed leading/trailing silence
- Applied noise reduction
- Standardization
- Converted to mono
- Resampled to 16,000 Hz
- Forced each clip to a uniform 5-second length
- 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.”*