Abstract / truncated to 115 words (read the full abstract)

Sound Source Localization (SSL) is the topic within acoustic signal processing which studies methods for the estimation of the position of one or more active sound sources in space, such as human talkers, using signals captured by one or more microphone arrays. It has many applications, including robot orientation, speech enhancement and diarization. Although signal processing-based algorithms have been the standard choice for SSL over past decades, deep neural networks have recently achieved state-of-the-art performance for this task. A drawback of most deep learning-based SSL methods consists of requiring the training and testing microphone and room geometry to be matched, restricting practical applications of available models. This is particularly relevant when using Distributed Microphone Arrays ... toggle 6 keywords

sound source localization (ssl) – deep neural networks (dnns) – machine learning (ml) – deep learning (dl) – direction-of-arrival (doa) estimation – microphone arrays

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