Blind Audio Source Separation Using Independent Component Analysis and Independent Vector Analysis

Year 2016, Special Issue (2016), 174 - 177, 01.12.2016

Alyaa Mahdi Ahmet Elbir Fethullah Karabiber

https://doi.org/10.18100/ijamec.270075

Abstract

Blind Source Separation (BSS) is one of the most important and
challenging problem for the researchers in audio and speech processing area. In
the literature, many different methods have been proposed to solve BSS problem.
In this study, we have compared the performance of three popular BSS methods
based on Independent Component Analysis (ICA) and Independent Vector Analysis
Models, which are Fast-ICA, Kernel-ICA and Fast-IVA. We collected experimental
data by recording speech from 13 people. Three different scenarios are proposed
to compare the performance of BSS methods effectively. Experimental results
show that the Fast-IVA has better performance than the ICA based methods
according to performance metrics of Source-to-Artifact Ratio,
Source-to-Distortion Ratio and Source-to-Noise Ratio. But ICA methods give
better results than Fast-IVA according to the Source-to-Interference Ratio.

 

Keywords

Blind source separation, Independent component analysis

References

• [1] S Haykin, Z Chen, The cocktail party problem” Neural Comput. 17,18751902 (2005).
• [2] J Herault, Jutten C, B Ans, Detection of primitive magnitudes in a Message composite by a neural computing architecture unsupervised learning, in Proc. GRETSI Vol 2 (Nice, France, 1985), pp. 1017-1022
• [3] Bell AJ, Sejnowski TJ, An information maximisation approach to blind separation and blind deconvolution. Neural Computation 7:11291159(1995).
• [4] Aapo Hyvarien and Erkki Oja laboratory fast fixed point independent compenent analysis this paper appear in neural computation 9 :14831492; 1997.
• [5] Sargam Parmar, Bhuvan Unhelkar Performance Comparisions of ICA Algorithms to DS-CDMA Detection journal of telecommunications, volume 1, issue 1,february 2010.
• [6] HongLi, Yunlian Sun, The Study and Test of ICA Algorithms 2005 IEEE.
• [7] Francis R. Bach, Michael I. Jordan, Kernel Independent Component Analysis. Journal of Machine Learning Research 3 1-48. (2002).
• [8] MS Pedersen, J Larsen, U Kjems, LC Parra. A survey of convolutive blind source separation methods, in Springer Handbook on Speech Processing and Speech Communication (Springer, New York, 2007),pp. 134.
• [9] T Kim, I Lee, TW Lee. Independent vector analysis: definition and algorithms, in Fortieth Asilomar Conference on Signals, Systems andComputers 2006 (Asilomar, USA, 2006), pp. 13931396.
• [10] Ganesh R. Naik and Dinesh K Kumar, An Overview of Independent Component Analysis and Its Applications , Informatica 35 (2011) 6381.
• [11] Yan feng Liang n, Jack Harris, Syed Mohsen Naqvi, Gaojie Chen,Jonathon A. Chambers , Independent vector analysis with a generalized multivariate Gaussian source prior for frequency domain blind source separation, at Science Direct Signal Processing 28May2014.
• [12] Emmanuel Vincent, Rmi Gribonval, and Cdric Fvotte, Performance Measurement in Blind Audio Source Separation, IEEE Transactions On Audio, Speech, And Language Processing, Vol. 14, No. 4, July 2006.
• [13] Manfred U.A. Bromba and Horst Ziegler, Application hint for Savitsky-Golay digital smoothing filters, Anal. Chem. 53, 15831586 (1981).
• [14] Francis R. Bach, Michael I. Jordan (2001). Kernel Independent Component Analysis, Technical Report UCB//CSD-01-1166, University of California, Berkeley.
• [15] L Parra, C Spence, Convolutive blind separation of non-stationary sources, IEEE Trans. Speech Audio Process. 8, 320327 (2000).