Audio Representation

Audio representation research focuses on developing effective ways to encode audio signals for machine understanding, aiming to create models that can process and interpret diverse sounds like speech, music, and environmental noises. Current research emphasizes self-supervised learning techniques, often employing transformer-based architectures or more efficient alternatives like state space models, to learn robust representations from large, unlabeled datasets. These advancements are crucial for improving various applications, including speech recognition, music information retrieval, sound event detection, and even healthcare applications like heart murmur detection, by enabling more accurate and efficient audio processing. The development of general-purpose audio representations that perform well across diverse audio domains remains a key focus.