Evaluation of two-channel source separation using exploratory projection pursuit technique

• Main Authors: Abrar Hussain, Kalaivani Chellapan, Siti Zamratol-Mai Sarah Mukari
• Format: Article
• Language: English
• Published: Penerbit Universiti Kebangsaan Malaysia 2018
• Online Access: http://journalarticle.ukm.my/12961/1/23977-77029-1-PB.pdf; http://journalarticle.ukm.my/12961/; http://ejournal.ukm.my/jskm/issue/view/1072
• Institution: Universiti Kebangsaan Malaysia

Difficulty of understanding speech in noise among the elderly necessitates the need for Auditory Training which has made a renewal of interest in the last decade with the auditory training applications. This interest is perhaps spurred by advances in computer-based technology. In computer-based auditory training, speech signals are considered as auditory training stimuli where input speech signals need to be verified prior to training as the speech signals are mixed with noise signals. Computer-based Auditory Training System can be embedded with input speech verifying module. Input speech verifying module is employed with speech and noise separator simulator. This simulator needs to guarantee accurate separation of speech from noise signals. Therefore, in this research, Exploratory Projection Pursuit (EPP) technique under semi-Blind Source Separation (BSS) method is intended to separate the speech source signals which are mixed with competing speech (multitalker speech babble). This training uses Malay language based sentences which differ in word length and hence number of sample values. The experimental simulation considers two-channel random, linear mixing of speech sources and competing speech. The aim of this study is to evaluate the performance of source separation using the anticipated EPP technique for various sample values of speech signals which varies in time duration due to word length dissimilarity. Simulation results show that EPP technique is feasible for source separation. As a consequence, high correlation value of r ≥ 0.99 is obtained between extracted speech signal and original speech signal for all categories of speech signals. It is further verified by the maximum nongaussianity of extracted speech signal which has high kurtosis value of 32 approximately.