In fact, FPGAs have been already considered by other researchers, mainly for converting the analogue or digital microphone signals into an audio format without further signal processing computation.
Current FPGAs are a potential solution thanks to their high-computational power and low latency response. This number is drastically increasing as low-cost MEMS technology is readily available. However, the computational demand is directly related to the number of microphones of the array. Most of the signal processing needed in microphone arrays is traditionally done using general purpose processors. Finally, we also propose different strategies to further accelerate the sound-source localization while offering increased angular resolution. Experiments demonstrate how an accurate sound-source localization is obtained in a couple of milliseconds, independently of the number of microphones.
In this paper, we present a Filter-and-Sum based architecture and several acceleration techniques to provide accurate sound-source localization in real-time. A higher number of microphones, however, increase the computational demand, making real-time response challenging. The response of such sensor arrays is dependent on the quantity of microphones operating on the array. The use of microphone arrays for sound-source localization is a well-researched topic.
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