A Multi-dimensional Deep Structured State Space Approach to Speech Enhancement Using Small-footprint Models

Author:

Pin-Jui Ku, Chao-Han Huck Yang, Sabato Marco Siniscalchi, Chin-Hui Lee

Keyword:

Electrical Engineering and Systems Science, Audio and Speech Processing, Audio and Speech Processing (eess.AS), Artificial Intelligence (cs.AI), Sound (cs.SD), Signal Processing (eess.SP), Systems and Control (eess.SY)

journal:

date:

2023-05-31 16:00:00

Abstract

We propose a multi-dimensional structured state space (S4) approach to speech enhancement. To better capture the spectral dependencies across the frequency axis, we focus on modifying the multi-dimensional S4 layer with whitening transformation to build new small-footprint models that also achieve good performance. We explore several S4-based deep architectures in time (T) and time-frequency (TF) domains. The 2-D S4 layer can be considered a particular convolutional layer with an infinite receptive field although it utilizes fewer parameters than a conventional convolutional layer. Evaluated on the VoiceBank-DEMAND data set, when compared with the conventional U-net model based on convolutional layers, the proposed TF-domain S4-based model is 78.6% smaller in size, yet it still achieves competitive results with a PESQ score of 3.15 with data augmentation. By increasing the model size, we can even reach a PESQ score of 3.18.

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