Sound Source

Sound source localization and separation are active research areas aiming to pinpoint and isolate individual sound sources from complex audio mixtures. Current research focuses on improving the accuracy and robustness of these techniques using deep neural networks, including convolutional neural networks, variational autoencoders, and transformer architectures, often incorporating multimodal data (audio-visual) and addressing challenges like reverberation, noise, and the presence of multiple sources. These advancements have significant implications for applications such as virtual and augmented reality, assistive listening devices, and improved audio recording and mixing technologies. Furthermore, developing reference-free evaluation metrics and addressing the limitations of existing models in handling negative audio cases are key challenges driving current research.