Abstract
In this paper, we propose a method for blind source separation (BSS) of convolutive audio recordings with short blocks of stationary sources, i.e. dynamically changing source activity but no source movements. It consists of a time-frequency sparseness based localization step to identify segments with stationary sources whose number is equal to the number of microphones. We then use a frequency domain independent component analysis (ICA) algorithm that is robust to short data segments to separate each identified segment. In each segment we solve the permutation problem using the state coherence transform (SCT). Experimental results using real room impulse responses show a good separation performance.
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Nesta, F., Omologo, M., Svaizer, P.: A novel robust solution to the permutation problem based on a joint multiple TDOA estimation. In: Proc. International Workshop for Acoustic Echo and Noise Control, IWAENC (2008)
Loesch, B., Nesta, F., Yang, B.: On the robustness of the multidimensional state coherence transform for solving the permutation problem of frequency-domain ICA. In: Proc. ICASSP (2010)
Masnadi-Shirazi, A., Zhang, W., Rao, B.D.: Glimpsing indepdendent vector analysis: Separation more sources than sensors using active and inactive states. In: Proc. ICASSP (2010)
Hsieh, H.-L., Chien, J.-T.: Online bayesian learning for dynamic source separation. In: Proc. ICASSP (2010)
Loesch, B., Yang, B.: Blind source separation based on time-frequency sparseness in the presence of spatial aliasing. Submitted to LVA/ICA (2010)
Chami, Z.E., Guerin, A., Pham, A., Serviere, C.: A phase-based dual microphone method to count and locate audio sources in reverberant rooms. In: Proc. IEEE Workshop on Applications of Signal processing to Audio and Acoustics, WASPAA (2009)
Nesta, F., Svaizer, P., Omologo, M.: Separating short signals in highly reverberant environment by a recursive frequency domain BSS. In: Proc. Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA) (May 2008)
Real World Computing Partnership, RWCP Sound Scene Database in Real Acoustic Environment (2001), http://tosa.mri.co.jp/sounddb/indexe.htm
Cummins, F., Grimaldi, M., Leonard, T., Simko, J.: The CHAINS corpus (characterizing individual speakers) (2006), http://chains.ucd.ie/
Vincent, E., Gribonval, R., Fevotte, C.: Performance measurement in blind audio source separation. IEEE Transactions on Speech and Audio Processing 14(4) (2006)
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Loesch, B., Yang, B. (2010). Adaptive Segmentation and Separation of Determined Convolutive Mixtures under Dynamic Conditions. In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2010. Lecture Notes in Computer Science, vol 6365. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15995-4_6
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DOI: https://doi.org/10.1007/978-3-642-15995-4_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15994-7
Online ISBN: 978-3-642-15995-4
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