A study on unsupervised monaural reverberant speech separation

Abstract

Separating individual source signals is a challenging task in musical and multitalker source separation. This work studies unsupervised monaural (co-channel) speech separation (UCSS) in reverberant environment. UCSS is the problem of separating the individual speakers from multispeaker speech without using any training data and with minimum information regarding mixing condition and sources. In this paper, state-of-art UCSS algorithms based on auditory and statistical approaches are evaluated for reverberant speech mixtures and results are discussed. This work also proposes to use multiresolution cochleagram and Constant Q Transform (CQT) spectrogram feature with two-dimensional Non-negative matrix factorization. Results show that proposed algorithm with CQT spectrogram feature gave an improvement of 1.986 and 1.262 in terms of speech intelligibility and 0.296 db and 0.561 db in terms of signal to interference ratio compared to state-of-art statistical and auditory approach respectively at T60 of 0.610s.