Speaker localization algorithms often assume static location for all sensors. This assumption simplifies the models used, since all acoustic transfer functions are linear time invariant. In many applications this assumption is not valid. In this paper we address the localization challenge with moving microphone arrays. We propose two algorithms to find the speaker position. The first approach is a batch algorithm based on the maximum likelihood criterion, optimized via expectationmaximization iterations. The second approach is a particle filter for sequential Bayesian estimation. The performance of both approaches is evaluated and compared for simulated reverberant audio data from a microphone array with two sensors.
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