Fast Low-Latency Convolution by Low-Rank Tensor Approximation

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Journal Title
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Volume Title
Conference article in proceedings
Date
2023-06-10
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Mcode
Degree programme
Language
en
Pages
5
1-5
Series
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
Abstract
In this paper we consider fast time-domain convolution, exploiting low-rank properties of an impulse response (IR). This reduces the computational complexity, speeding up the convolution, without introducing latency. Previous work has considered a truncated singular value decomposition (SVD) of a two-dimensional matricization, or reshaping, of the IR. We here build upon this idea, by providing an algorithm for convolution with a three-dimensional tensorization of the IR. We provide simulations using real-life acoustic room impulse responses (RIRs) of various lengths, convolving them with music, as well as speech signals. The proposed algorithm is shown to outperform the comparable existing algorithm in terms of signal quality degradation, for all considered scenarios, without increasing the computational complexity, or the memory usage.
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Keywords
Degradation, Tensors, Convolution, Computational modeling, Signal processing algorithms, Approximation algorithms, Acoustics
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Citation
Jälmby , M , Elvander , F & Waterschoot , T V 2023 , Fast Low-Latency Convolution by Low-Rank Tensor Approximation . in ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . , 10095908 , Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing , IEEE , pp. 1-5 , IEEE International Conference on Acoustics, Speech, and Signal Processing , Rhodes Island , Greece , 04/06/2023 . https://doi.org/10.1109/ICASSP49357.2023.10095908