What is Audio Classification
Audio Classification - Categorizing audio clips by content type or event
A task that assigns category labels to audio recordings based on their content, such as environmental sounds, music genres, or speech types. Audio classification enables automatic tagging and filtering of audio content in multimodal processing pipelines.
How It Works
Audio classification models convert raw audio into a spectrogram representation, which is processed by a neural network to predict category labels. The audio waveform is transformed into a time-frequency representation (mel spectrogram), then fed through a convolutional or transformer-based classifier that outputs probabilities over predefined categories.
Technical Details
Models like AST (Audio Spectrogram Transformer), PANNs, and BEATs achieve strong results on AudioSet (527 event classes). Input is typically 10-second audio segments converted to 128-band mel spectrograms. Transfer learning from models pretrained on AudioSet is standard for domain-specific classification tasks. Multi-label classification handles audio containing multiple simultaneous events. Performance is measured using mAP (mean Average Precision).
Best Practices
- Use pretrained models (AST, BEATs) and fine-tune on domain-specific audio categories
- Apply data augmentation (time stretching, pitch shifting, SpecAugment) for robust training
- Handle multi-label scenarios since real-world audio often contains multiple simultaneous events
- Classify audio segments alongside visual and text features for comprehensive multimodal indexing
Common Pitfalls
- Training on clean studio audio and deploying on noisy real-world recordings
- Not segmenting long audio files, causing the model to miss short events
- Using music-trained models for environmental sound classification or vice versa
- Ignoring class imbalance in audio datasets where common sounds dominate
Advanced Tips
- Use audio event detection (with temporal localization) instead of clip-level classification for precision
- Combine audio classification with visual classification for audio-visual event detection
- Implement weakly supervised learning to train classifiers from video-level labels
- Apply zero-shot audio classification using CLAP for categories not seen during training