Plug-and-play learnable EMD module for time-domain speech separation

Abstract

This paper presents a learnable empirical mode decomposition (EMD) module tailored for single channel speech separation. The proposed EMD module exhibits a flexible plug-and-play characteristic, enabling its integration into various speech separation networks. Conventionally, non-learnable EMD algorithms, as a nonlinear time-domain adaptive techniques, aimed at decomposing speech signals into a set of oscillatory intrinsic mode functions (IMFs) and a residual component. However, the inherent non-differentiability of the interpolation operation in these algorithms restricts their integration into neural networks, thus the EMD algorithm only exists in the preprocessing step and cannot be integrated into the neural network to form an end-to-end trainable speech separation network. In order to mitigate this issue, we use the max pooling layer and the deconvolution network to replace the envelope solving algorithm in the EMD method and build a trainable EMD neural network module. Multiple sets of experiments have shown that the integrating of EMD module brings the improvement to different separation systems.