Abstract
For intermittent channel output signal, namely, the active periods followed by nonactive periods alternatively, the blind single-input single-output (SISO) system identification problem can be transformed into a blind multichannel identification problem. It is possible and feasible to blindly identify the channel using only second-order statistics from the channel output signal. A two-stage approach is proposed in this paper. At the first stage, two or more segments of channel input signal are estimated from the single channel observation; at the second stage, the channel impulse response is identified by exploiting the estimated channel input signal segments and their corresponding channel output signal segments. Simulations show that the proposed approach works well.
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References
Xu, G., Liu, H., Tong, L., Kailath, T.: A least-square approach to blind channel identification. IEEE Trans. on Signal Processing 43(12), 2982–2992 (1995)
Mei, T., Mertins, A., Kallinger, M.: Room impulse response shortening with infinity-norm optimization. In: Proc. IEEE Int. Conf. Acoust. Speech and Signal Processing, pp. 3745–3748 (2009)
Shabtai, N., Rafaely, B., Zigel, Y.: Room Volume Classification from Reverberant Speech. In: Proceedings of International Workshop on Acoustic Echo and Noise Control (IWAENC 2010), Tel Aviv (2010)
Filos, J., Habets, E., Naylor, P.A.: A Two-step Approach to Blindly Infer Room Geometries. In: Proceedings of International Workshop on Acoustic Echo and Noise Control (IWAENC 2010), Tel Aviv (2010)
Tong, L., Xu, G., Kailath, T.: Blind Identification and Equalization Based on Second-Order Statistics: A Time Domain Approach. IEEE Trans. on Information Theory 40(2), 340–348 (1994)
Huang, Y., Benesty, J.: Adaptive Multi-channel Least Mean Square and Newton Algorithms for Blind Channel Identification. Signal Processing 82, 1127–1138 (2002)
Huang, Y., Benesty, J.: A Class of Frequency-Domain Adaptive Approaches to Blind Multichannel Identification. IEEE Trans. on Signal Processing 51(1), 11–24 (2003)
Allen, J.B., Berkley, D.A.: Image method for efficiently simulating small-room acoustics. Journal of the Acoustical Society of America 65(4), 943–950 (1979)
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Mei, T. (2013). Blind Single Channel Identification Based on Signal Intermittency and Second-Order Statistics. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_60
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DOI: https://doi.org/10.1007/978-3-642-39068-5_60
Publisher Name: Springer, Berlin, Heidelberg
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