Adaptive time segmentation for improved signal model parameter estimation for a single-source scenario

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Estimating the parameters that describe the acoustic scene is very important for many microphone array applications. For example, consider the power spectral densities (PSDs) or relative acoustic transfer functions (RTFs) that are required when estimating a particular sound source using multi-microphone noise reduction. State-of-the-art algorithms estimate the pa-rameters per segment, where each segment consists of a fixed number of time frames. These algorithms exploit the assumption that PSDs are constant per time frame, and RTFs are constant per segment. However, in practice, sound sources will move relative to the microphone array. Improved per-formance is therefore expected when the actual time frames that are used to form the segments are adapted such that time frames all share the same (unknown) RTF. In this paper, we therefore present an algorithm to obtain an optimal adaptive time segmentation and combine this with our previously pub-lished joint maximum likelihood estimator (JMLE) for jointly estimating the RTF, source PSD and late reverberation PSD of a single source in a reverberant environment.