Interpreting End-to-End Deep Learning Models for Speech Source Localization Using Layer-wise Relevance Propagation

Deep learning models for audio processing can be made transparent: The researchers successfully used Layer-wise Relevance Propagation (LRP) to open the "black box" of two different deep learning models designed to locate sound sources, revealing which parts of the audio input were most important for making predictions.

AI systems learn to clean up messy audio data: Both models analyzed in the study automatically learned to remove background noise and echo (reverberation) from microphone signals before locating sound sources, suggesting that AI systems develop practical signal-processing strategies similar to those used in traditional audio engineering.

Explainability can improve AI performance: When the researchers extracted the "relevance signals" that the neural networks had learned to prioritize, these signals produced more accurate time-delay estimates than the original microphone data alone, demonstrating that understanding why an AI model makes decisions can lead to better results.

Explainability techniques work across different model architectures: The LRP method successfully interpreted two different neural network designs with varying complexity levels, suggesting that transparency tools may be broadly applicable across different AI systems rather than being limited to specific model types.