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Speech Separation On Whamr

Métriques

SI-SDRi

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
SI-SDRi
Paper TitleRepository
SepReformer-L + DM17.1Separate and Reconstruct: Asymmetric Encoder-Decoder for Speech Separation-
Bi-LSTM-TASNET9.2WHAM!: Extending Speech Separation to Noisy Environments-
DPTNET - SRSSN12.3Stepwise-Refining Speech Separation Network via Fine-Grained Encoding in High-order Latent Domain-
TD-Confomer (S)10.5On Time Domain Conformer Models for Monaural Speech Separation in Noisy Reverberant Acoustic Environments-
MossFormer (L) + DM16.3MossFormer: Pushing the Performance Limit of Monaural Speech Separation using Gated Single-Head Transformer with Convolution-Augmented Joint Self-Attentions-
TD-Conformer (L) + DM13.4On Time Domain Conformer Models for Monaural Speech Separation in Noisy Reverberant Acoustic Environments-
VSUNOS12.2Voice Separation with an Unknown Number of Multiple Speakers-
DPRNN - SRSSN12.3Stepwise-Refining Speech Separation Network via Fine-Grained Encoding in High-order Latent Domain-
TF-Locoformer (M)18.5TF-Locoformer: Transformer with Local Modeling by Convolution for Speech Separation and Enhancement-
Deformable TCN + Dynamic Mixing11.1Deformable Temporal Convolutional Networks for Monaural Noisy Reverberant Speech Separation-
TD-Conformer (XL) + DM14.6On Time Domain Conformer Models for Monaural Speech Separation in Noisy Reverberant Acoustic Environments-
Wavesplit13.2Wavesplit: End-to-End Speech Separation by Speaker Clustering-
Improved Sudo rm -rf (U=36)13.5Compute and memory efficient universal sound source separation-
Deformable TCN + Shared Weights + Dynamic Mixing10.1Deformable Temporal Convolutional Networks for Monaural Noisy Reverberant Speech Separation-
MossFormer217.0MossFormer2: Combining Transformer and RNN-Free Recurrent Network for Enhanced Time-Domain Monaural Speech Separation-
Sudo rm -rf (U=16)12.1Sudo rm -rf: Efficient Networks for Universal Audio Source Separation-
TF-Locoformer (S)17.4TF-Locoformer: Transformer with Local Modeling by Convolution for Speech Separation and Enhancement-
TD-Confomer (M) + DM12On Time Domain Conformer Models for Monaural Speech Separation in Noisy Reverberant Acoustic Environments-
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Speech Separation On Whamr | SOTA | HyperAI