This paper presents a maximum likelihood (ML) framework for multimicrophone

sound source localization (SSL). Besides deriving the

framework, we focus on making the connection and contrast between

the ML-based algorithm and popular steered response power

(SRP) SSL algorithms such as phase transform (SRP-PHAT). We

also show under our ML framework how challenging conditions

such as directional microphone arrays and reverberations can be handled.

The computational cost of our method is low – similar to SRPPHAT.

The effectiveness of the proposed method is shown on a large

dataset with 99 real-world audio sequences recorded by directional

circular microphone arrays in over 50 different meeting rooms.

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In  ICASSP

Publisher  IEEE

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