978-1-4673-7231-2/15/$31.00 ©2015 IEEE Blind Audio Source Separation Using Wiener Filtering Approach Pardeep Sharma 1 , Rajesh Mehra 2 , Naveen Dubey 3 1,2,3 Department of Electronics and Communication Engineering, National Institute for Technical Teachers Training and Research Sector – 26, Chandigarh, 16001, India 1 Pardeep_4992@yahoo.in 2 rajeshmehra@yahoo.com 3 naveendubey@rkgitw.edu.in Abstract- Audio Source separation techniques are used for better reception of sound and speech signals. Wiener filtering tool is best and one of the principally used method in separation of the audio signal from mixture of source signals. As the STFT utilizes short-duration stationarity in time frequency domain, we use Wiener filtering mask which does not depends on the consistency of the output for the voice gram and is different from the STFT in time-frequency domain. Short-time Fourier transform (STFT) in time-frequency domain is used if processing is done on audio signals. In this paper a technique for blind audio source separation using Wiener filtering algorithm is presented and result reflects that it serves good quality of separation in comparison of classical ICA algorithms like fast ICA, JADE. So it gives the SIR value 6.68% and 39.22% higher than that of fast ICA and JADE respectively. Keywords- Blind audio source separation, Wiener filtering, Independent component analysis (ICA), Short time Fourier transforms. I. INTRODUCTION ASS basically known as blind audio source separation or blind audio signal separation used to recover the individual/independent source signals from the mixture of signals. Suppose R(t)= [r 1(t) , r 2(t), ----, r n(t) ] be the source signals and M(t) = [m 1(t) , m 2(t), ------ , m n(t) ] be the mixed signal received at different microphones located at different positions. So that the actual mixing process received at the mixer end will be expressed by M(t) = A(t)*R(t). Let take an example of the group discussion problem, where voices from individual persons are mixed and it is difficult to listen to the one actually speaking and need to be separate out the desired one. So for this a processing system can be developed to obtain the desired voice from the rest of the speakers. In audio scenario, the signals which are non stationary, audio processing can be done by using short-time Fourier transform (STFT). Here the signal is taken in time-frequency domain [1]. An application which grows abruptly in various fields i.e. speech processing, telecommunication and many more [2]. Here the separated signal is the mixture form of interference which includes the voice interference or a noise generated in the path of communication and the estimation of the variance of that signal is not desired as compared to the presented signal [3]. In digital signal processing, there are different Wiener algorithms, which include maximum likelihood estimation and negentropy maximization. These are the computational methods for separating a mixed signal into additive non-Gaussian sub-elements. The signal comes out of this method is complex and this complexity is greater than its applied source signal. The problem of this blind convolutive mixture is sort-out from the algorithm popularly known as Wiener filtering approach. This is based on fixed point algorithm iteration to maximize nongaussianity to a great extent as a measure of statistical independence. In this paper we propose implementation of blind audio source separation using the dedicated approach popularly known as Wiener filtering, which is used to recover two or more audio source signals obtained from unknown linear combinations. This work proposes a Wiener filtering technique for ICA algorithm which is having better quality of separation and fast convergence in terms of signal to interference ratio. The rest of this paper is formed as. In the next section, we define blind audio source separation (BASS). Section III describes the Weiner filtering technique and algorithm for blind audio source separation. Section IV produces the Results and discussion. The conclusion of this paper and improved separation effect are given in Section V. II. BASS TECHNIQUE The purpose of BASS is to recover audio (source) signal from a mixture of different audio signal generated by a number of B