A Comparative Study of Blind Source Separation for Bioacoustics Sounds based on FastICA, PCA and NMF

Blind Source Separation (BSS) is a task of separating a set of source signals from mixed signal without (or very little information) of both the sources and the mixing process. This paper addresses the problem of BSS in bio-acoustic mixed signals. In a noisy acoustic environment, animal species recognition based on vocalization remains a challenging task. In order to robustly recognize the specific species, the source signals of interest need to be separated from the mixed signals. This separation process is a significant pre-processing step before the recognition process takes place. In this paper, three different source separation methods namely Fast Fixed-Point Independent Component Analysis algorithms (FastICA), Principal Component Analysis (PCA) and Non-Negative Matrix Factorization (NMF) are implemented. In this experiment, the mixtures of frog sound signals are used as input. The quality of separated source signals using FastICA, PCA and NMF algorithms are compared and evaluated according to BSSEVAL toolbox metrics. These metrics consist of signal to distortion ratio (SDR), signal to interference ratio (SIR) and signal to artifacts ratio (SAR). The results show that FastICA with negentropy technique for finding a maximum non-gaussianity has the best performances in separating mixed signals.