Cha Zhang, Zhengyou Zhang, and Dinei Florencio
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.