AttHear: Explaining Audio Transformers Using Attention-Aware NMF

The increasing success of transformer models in various fields, such as computer vision and audio processing, has led to a growing need for improved explainability to understand their complex decision-making processes better. Most existing techniques for explaining transformer models concentrate primarily on delivering visual and textual explanations, commonly used in visual media. However, audio explanations are crucial due to their intuitiveness on audio-based tasks and distinguishing expressiveness over other modalities. This work proposes a novel method to interpret audio-processing transformer models. Our method combines the available attention mechanism inside these models with non-negative matrix factorisation (NMF) to compute relevancy for audio inputs. While NMF decomposes audio into spectral patterns, attention weights are utilised to calculate time activation for these spectral patterns. The method then generates listenable audio explanations for the model’s final decision using the most relevant audio portions. Our model effectively generates explanations by benchmarking against standard datasets, including keyword spotting and environmental sound classification.