DeepAudioClassification

The project converts raw audio waveforms to mel-spectrogram images and applies image classification architectures to the resulting visual representationsa technique that has proven highly effective for audio classification tasks because CNNs can learn frequency and temporal patterns in spectrograms that are discriminative across audio classes.

The implementation covers the full pipeline from raw audio to classification result: audio loading and resampling, mel-spectrogram feature extraction with configurable frequency bins and time windows, CNN model training with data augmentation strategies adapted for spectrograms (time stretching, frequency masking, mixup), and inference serving for classifying new audio clips.

The project includes pre-trained models for common audio classification datasets and provides training scripts for fine-tuning on custom audio categories.

Sound engineers building audio tagging and monitoring systems, researchers working on environmental sound classification, music genre recognition, or speech accent identification, and developers building applications that respond to specific sound events use DeepAudioClassification as a reference implementation and starting point.

The CNN-on-spectrogram approach is particularly approachable for practitioners who already understand image classificationthe same architectural intuitions apply, with the spectrogram's frequency axis analogous to spatial height and the time axis analogous to spatial width.