Polynomial matrix eigenvalue decomposition of spherical harmonics for speech enhancement

Neo, Vincent W., Evers, Christine and Naylor, Patrick (2021) Polynomial matrix eigenvalue decomposition of spherical harmonics for speech enhancement. In IEEE International Conference on Acoustics, Speech, & Signal Processing (ICASSP). IEEE. 5 pp . (In Press)

Record type: Conference or Workshop Item (Paper)

Abstract

Speech enhancement algorithms using polynomialmatrixeigenvalue decomposition (PEVD) have been shown to be effective for noisy and reverberant speech. However, these algorithms do not scale well in complexity with the number of channels used in the processing. For a spherical microphone array sampling an order-limited sound ﬁeld, the spherical harmonics provide a compact representation of the microphone signals in the form of eigenbeams. We propose a PEVD algorithm that uses only the lower dimension eigenbeams for speech enhancement at a signiﬁcantly lower computation cost. The proposed algorithm is shown to signiﬁcantly reduce complexity while maintaining full performance. Informal listening examples have also indicated that the processing does not introduce any noticeable artefacts.

Text

[ICASSP2021]_PEVD_of_Spherical_Harmonics_for_Speech_Enhancement_final - Accepted Manuscript

Available under License University of Southampton Accepted Manuscript Licence.

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