Ad Hoc Microphone Array

Ad hoc microphone arrays, comprised of arbitrarily placed microphones, are being actively researched to improve sound source localization and speech enhancement in challenging acoustic environments. Current work focuses on deep learning models, including masked autoencoders, neural networks integrated with traditional methods like Steered Response Power (SRP), and graph convolutional networks, to address issues like reverberation, noise, and microphone asynchronicity. These advancements aim to improve the accuracy and robustness of speech processing applications, such as speaker verification and speech recognition, in scenarios where traditional fixed microphone arrays are impractical or infeasible. The resulting improvements have significant implications for applications ranging from hands-free voice control to robust audio conferencing.