Abstract
Frequency domain ICA has been used successfully to separate the utterances of interfering speakers in convolutive environments, see e.g. [6],[7]. Improved separation results can be obtained by applying a time frequency mask to the ICA outputs. After using the direction of arrival information for permutation correction, the time frequency mask is obtained with little computational effort. The proposed postprocessing is applied in conjunction with two frequency domain ICA methods and a beamforming algorithm, which increases separation performance for reverberant, as well as for in-car speech recordings, by an average 3.8dB. By combined ICA and time frequency masking, SNR-improvements up to 15dB are obtained in the car environment. Due to its robustness to the environment and regarding the employed ICA algorithm, time frequency masking appears to be a good choice for enhancing the output of convolutive ICA algorithms at a marginal computational cost.
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Kolossa, D., Orglmeister, R. (2004). Nonlinear Postprocessing for Blind Speech Separation. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_105
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DOI: https://doi.org/10.1007/978-3-540-30110-3_105
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