Definition
Audio classification is the process of assigning labels to audio recordings based on their content. Categories may include speech, music, animal sounds, alarms, or environmental noise.
Purpose
The purpose is to automate recognition and categorization of sound, making audio searchable and analyzable by AI. It is widely used in safety systems, media organization, and assistive technologies.
Importance
- Enables automation in speech, music, and sound recognition.
- Improves accessibility through audio-based interfaces.
- Relies on diverse training data for accuracy across conditions.
- Errors can affect safety-critical applications (e.g., alarms).
How It Works
- Capture or import raw audio signals.
- Extract features such as spectrograms or MFCCs.
- Train classifiers (e.g., neural networks) on labeled data.
- Evaluate accuracy against test sets.
- Deploy models for real-time or batch classification.
Examples (Real World)
- Shazam: identifies music tracks from short audio clips.
- Google Sound Classifier: detects everyday sounds like barking or sirens.
- BirdNET: identifies bird species based on recorded songs and calls.
References / Further Reading
- Audio Classification with Machine Learning — TensorFlow.
- Environmental Sound Classification with CNNs — IEEE (Piczak, 2015).
- Machine Learning for Audio Signal Processing — MIT OpenCourseWare.