3 accepted papers
The increasing success of audio foundation models across various tasks has led to a growing need for improved interpretability to understand their intricate decision-making processes better. Existing methods primarily focus on explaining these models by attributing importance to elements within the…
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 pri…
Despite significant advancements in deep learning for vision and natural language, unsupervised domain adaptation in audio remains relatively unexplored. We, in part, attribute this to the lack of an appropriate benchmark dataset. To address this gap, we present Synthia’s melody, a novel audio data…