Co-channel speech separation using frequency bin non-linear adaptive filtering

An approach to speech separation via frequency bin nonlinear adaptive filtering is proposed. The algorithm is proved effective for speech intelligibility enhancement over a TIR (target-to-interference energy ratio) range between 0 dB and ±12 dB. Informal listening tests and spectrogram comparisons are discussed. In addition, a robust multi-pitch contour estimation via HMMs (hidden Markov models) is investigated. This HMM pitch estimator is combined with a pseudo-perceptual pitch estimator in order to simultaneously estimate two speakers´ pitch from summed signals