Abstract
The purpose of blind speech deconvolution is to recover both the original speech source and the room impulse response (RIR) from the observed reverberant speech. This can be beneficial for improving speech intelligibility and speech perception. However, the problem is ill-posed, which often requires additional knowledge to solve. In order to address this problem, prior information (such as the sparseness of signal or acoustic channel) is often exploited. In this paper, we propose a joint \(L1-L2\) regularisation based blind speech deconvolution method for a single-input and single-output (SISO) acoustic system with a high level of reverberation, where both the sparsity and density of the room impulse responses (RIR) are considered, by imposing an L1 and L2 norm constraint on their early and late part respectively. By employing an alternating strategy, both the source signal and early part in the RIR can be well reconstructed while the late part of the RIR can be suppressed.
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Acknowledgements
The work was conducted when J. Guan visited the Centre for Vision, Speech and Signal Processing (CVSSP), the University of Surrey. This work was supported in part by International Exchange and Cooperation Foundation of Shenzhen City, China (No. GJHZ20150312114149569).
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Guan, J., Wang, X., Xie, Z., Qi, S., Wang, W. (2018). Joint \(L1-L2\) Regularisation for Blind Speech Deconvolution. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_80
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