Abstract
This paper presents a method for enhancing a target speech in the presence of a jammer and background diffuse noise. The method is based on frequency domain blind signal separation (FD-BSS). In particular, the permutation resolution is done using both the direction of arrival (DOA) information contained in the estimated filters and some statistical features computed on the estimated signals. This enables the separation of the target speech, the jammer and the diffuse background noise which is not possible if using only the DOA or the statistical features. Since in presence of diffuse noise, FD-BSS cannot provide a good estimate of the target speech a channel wise modified Wiener filter is proposed as post processing to further enhance the target speech.
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References
Pedersen, M., Larsen, J., Kjems, U., Parra, L.: A Survey of Convolutive Blind Source Separation Methods. Springer, Heidelberg (2007)
Takahashi, Y., Saruwatari, H., Shikano, K.: Real-time implementation of blind spatial subtraction array for hands-free robot spoken dialogue system. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France, pp. 1687–1692 (2008)
Sawada, H., Mukai, R., Araki, S., Makino, S.: A robust and precise method for solving the permutation problem of frequency-domain blind source separation. IEEE Trans. Speech and Audio Processing 12, 530–538 (2004)
Even, J., Saruwatari, H., Shikano, K.: An improved permutation solver for blind signal separation based front-ends in robot audition. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France, pp. 2172–2177 (2008)
Takahashi, Y., Takatani, T., Saruwatari, H., Shikano, K.: Blind spatial subtraction array with independent component analysys for hands-free speech recognition. In: International Work Shop on Acoustic Echo and Noise Control (IWAENC) (CD-ROM) (2006)
Takahashi, Y., Osako, K., Saruwatari, H., Shikano, K.: Blind source extraction for hands-free speech recognition based on wiener filtering and ica-based noise estimation. In: Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSMCA), pp. 164–167 (2008)
Comon, P.: Independent component analysis, a new concept? Signal Processing 36, 287–314 (1994)
Saruwatari, H., Kurita, S., Takeda, K., Itakura, F., Nishikawa, T., Shikano, K.: Blind source separation combining independent component analysis and beamforming. EURASIP Journal on Applied Signal Processing 2003(11), 1135–1146 (2003)
Murata, N., Ikeda, S., Zieh, A.: An approach to blind source separation based on temporal structure of speech signals. Neurocomputing 41(1-4), 1–24 (2001)
Vlassis, N., Motomura, Y.: Efficient source adaptivity in independent component analysis. IEEE Trans. Neural Networks 12(3), 559–566 (2001)
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Even, J., Saruwatari, H., Shikano, K. (2009). Target Speech Enhancement in Presence of Jammer and Diffuse Background Noise. In: Adali, T., Jutten, C., Romano, J.M.T., Barros, A.K. (eds) Independent Component Analysis and Signal Separation. ICA 2009. Lecture Notes in Computer Science, vol 5441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00599-2_71
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DOI: https://doi.org/10.1007/978-3-642-00599-2_71
Publisher Name: Springer, Berlin, Heidelberg
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