Dual-path RNN [351] uses RNN architecture to perform speech separation. The model uses a
dual-path structure [351] to capture low-frequency and high-frequency information in the input
signal. Dual-path RNN achieves impressive performance in various speech separation tasks. The
advantage of this model is its ability to capture low-frequency and high-frequency information, but
its disadvantage is its high computational cost. Gated DualPathRNN [387] is a variant of Dual-path

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Mehrish et al.

Table 11. Table comparing the performance of different speech separation methods using SI-SDRi metrics on
various speech separation benchmarks.

WSJ0-2mix WSJ0-3mix WSJ0-5mix Libri2Mix Libri5Mix Libri10Mix Libri20Mix WHAM

Architecture

Model
Separate And Diffuse [357] Diffusion
MossFormer (L) [663]
MossFormer (M) [663]
SepFormer [518]
Sandglasset [283]
Hungarian PIT [120]
TDANet (L) [308]
TDANet [308]
Sepit [356]
Gated DualPathRNN [387] CNN + LSTM
Dual-path RNN [351]
Conv-Tasnet [353]
AReviewofDeepLearningTechniquesforSpeechProcessing