Speaker localization for far-field and near-field wideband sources using neural networks

Many applications such as hands-free videoconferencing, speech processing in large rooms, and acoustic echo cancellation, use microphone arrays to track speaker locations in real-time. A speaker is a wide-band source which may be in the near field or far field of the array. Current source localization approaches based oil neural networks can meet real-time constraints but assume far-field narrowband sources. In this paper, we (1) apply neural networks for determining direction-of-arrival for near-field and far-field wideband speaker localization, and (2) compute the instantaneous cross-power spectra between adjacent pairs of sensors to form the feature vector. We optimized the overall speaker localization system off-line to yield an absolute error of less than 6 degrees at all SNR of 10 dB and a sampling rate of 8000 Hz at each sensor. When performing speaker localization in real-time, the system would require 1 MFLOP/s.