An adaptive single channel EMD-TNMF blind source separation algorithm for both instantaneous and convolutive mixed signal

Abstract

Single channel blind source separation (SCBSS) using time-frequency nonnegative matrix factorization (NMF) has some shortcomings, where the source number and convolution order must be known and it’s sensitive to the factors (e.g. window function, window length and overlap ratio of adjacent windows). To compensate these shortcomings that cannot meet the real application, we proposed an adaptive EMD-TNMF algorithm, which can estimate both the source number and the convolution order and is applicable to both single channel linear instantaneous and convolutive mixtures. Firstly, the single channel signal is mapped into multiple channels by utilizing empirical mode decomposition (EMD), and the number of independent sources is estimated by applying eigenvalue descent ratio of the IMFs’ covariance matrix. Secondly, the autocorrelation-based method is used to estimate the convolution order. Thirdly, the nonnegative matrix is constructed by adding one positive matrix (all elements are positive). Finally, in time domain, NMF algorithm is used to separate source signals. The algorithm’s performance is verified by two experiments where the single channel signal is linear instantaneous mixing of four artificial signals and linear convolutive mixing of two speech signals respectively. Results show that this algorithm can estimate the source number and convolution order correctly and obtain better separated source signals.