What is Audio-Event-Detection & Clustering?
The Audio Event Detection (AED) and Clustering analyses aim to automatically detect and categorize sounds in large audio datasets without supervision. Our AED-C pipeline uses an AI-based clustering method (DBSCAN), which shows higher performance over other methods (e.g., k-means).
This analysis provides an efficient way to summarize and explore the sound categories in an audio dataset. The potential uses include:
- Quickly identifying communities of species
- Estimating species richness and composition
- Discovering unknown sound categories
- Quickly searching for examples of a desired signal/call, without the need for any existing examples
- Collecting training data for supervised audio recognition models
- Pattern Matching can be used after AED & Clustering to efficiently detect more examples of a desired sound
The pipeline consists of two main steps:
- Audio Event Detection (AED): automatically detect relevant sounds in raw field recordings (learn how to run AED here)
- Clustering (C): cluster these sounds based on feature similarities (learn how to run clustering here)
Learn more about the AED-C pipeline in our white paper.