2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDAUD-28.4
Paper Title POLYNOMIAL MATRIX EIGENVALUE DECOMPOSITION OF SPHERICAL HARMONICS FOR SPEECH ENHANCEMENT
Authors Vincent W. Neo, Imperial College London, United Kingdom; Christine Evers, University of Southampton, United Kingdom; Patrick A. Naylor, Imperial College London, United Kingdom
SessionAUD-28: Acoustic Sensor Array Processing 2: Beamforming
LocationGather.Town
Session Time:Friday, 11 June, 11:30 - 12:15
Presentation Time:Friday, 11 June, 11:30 - 12:15
Presentation Poster
Topic Audio and Acoustic Signal Processing: [AUD-SEN] Signal Enhancement and Restoration
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Speech enhancement algorithms using polynomial matrix eigenvalue 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.