| TF-Locoformer (L) + DM |
- SI-SDRi
- 25.1
- SDRi
- 25.2
- Number of parameters (M)
- 22.5
|
TF-Locoformer: Transformer with Local Modeling by Convolution for Speech Separation and Enhancement 2024-08-06 |
코드 1 |
| SepReformer-L |
- SI-SDRi
- 25.1
- SDRi
- 25.2
- Number of parameters (M)
- 59.4
- MACs (G)
- 155.5
|
Separate and Reconstruct: Asymmetric Encoder-Decoder for Speech Separation 2024-06-10 |
코드 1 |
| TF-Locoformer (M) + DM |
- SI-SDRi
- 24.6
- SDRi
- 24.7
- Number of parameters (M)
- 15.0
|
TF-Locoformer: Transformer with Local Modeling by Convolution for Speech Separation and Enhancement 2024-08-06 |
코드 1 |
| TF-Locoformer (L) |
- SI-SDRi
- 24.2
- SDRi
- 24.3
- Number of parameters (M)
- 22.5
|
TF-Locoformer: Transformer with Local Modeling by Convolution for Speech Separation and Enhancement 2024-08-06 |
코드 1 |
| MossFormer2 (L) |
- SI-SDRi
- 24.1
- Number of parameters (M)
- 55.7
|
MossFormer2: Combining Transformer and RNN-Free Recurrent Network for Enhanced Time-Domain Monaural Speech Separation 2023-12-19 |
코드 1 · 코드 2 |
| SepTDA (L=12) |
- SI-SDRi
- 24.0
|
Boosting Unknown-number Speaker Separation with Transformer Decoder-based Attractor 2024-01-23 |
연결 없음 |
| Separate And Diffuse |
- SI-SDRi
- 23.9
|
Separate And Diffuse: Using a Pretrained Diffusion Model for Improving Source Separation 2023-01-25 |
연결 없음 |
| TF-Locoformer (M) |
- SI-SDRi
- 23.6
- SDRi
- 23.8
- Number of parameters (M)
- 15.0
|
TF-Locoformer: Transformer with Local Modeling by Convolution for Speech Separation and Enhancement 2024-08-06 |
코드 1 |
| TF-Locoformer (S) + DM |
- SI-SDRi
- 22.8
- SDRi
- 23
- Number of parameters (M)
- 5.0
|
TF-Locoformer: Transformer with Local Modeling by Convolution for Speech Separation and Enhancement 2024-08-06 |
코드 1 |
| MossFormer (L) + DM |
- SI-SDRi
- 22.8
- Number of parameters (M)
- 42.1
- MACs (G)
- 86.1
|
MossFormer: Pushing the Performance Limit of Monaural Speech Separation using Gated Single-Head Transformer with Convolution-Augmented Joint Self-Attentions 2023-02-23 |
코드 1 · 코드 2 |
| SepMamba + DM (M) |
- SI-SDRi
- 22.7
- SDRi
- 22.9
|
SepMamba: State-space models for speaker separation using Mamba 2024-10-28 |
코드 1 |
| SPGM + DM |
- SI-SDRi
- 22.7
- Number of parameters (M)
- 26.2
- MACs (G)
- 77
|
SPGM: Prioritizing Local Features for enhanced speech separation performance 2023-09-22 |
코드 1 |
| MossFormer (M) + DM |
- SI-SDRi
- 22.5
|
MossFormer: Pushing the Performance Limit of Monaural Speech Separation using Gated Single-Head Transformer with Convolution-Augmented Joint Self-Attentions 2023-02-23 |
코드 1 · 코드 2 |
| SepIt |
- SI-SDRi
- 22.4
|
SepIt: Approaching a Single Channel Speech Separation Bound 2022-05-24 |
연결 없음 |
| SepFormer |
- SI-SDRi
- 22.3
- SDRi
- 22.4
|
Attention is All You Need in Speech Separation 2020-10-25 |
코드 1 · 코드 2 · 코드 3 · 코드 4 |
| Wavesplit v2 |
- SI-SDRi
- 22.2
- SDRi
- 22.3
|
Wavesplit: End-to-End Speech Separation by Speaker Clustering 2020-02-20 |
연결 없음 |
| SPGM |
- SI-SDRi
- 22.1
- Number of parameters (M)
- 26.2
- MACs (G)
- 77
|
SPGM: Prioritizing Local Features for enhanced speech separation performance 2023-09-22 |
코드 1 |
| TF-Locoformer (S) |
- SI-SDRi
- 22
- SDRi
- 22.1
- Number of parameters (M)
- 5.0
|
TF-Locoformer: Transformer with Local Modeling by Convolution for Speech Separation and Enhancement 2024-08-06 |
코드 1 |
| DPTNet (Libri1Mix speech enhancement pre-trained) |
- SI-SDRi
- 21.3
- SDRi
- 21.5
|
Stabilizing Label Assignment for Speech Separation by Self-supervised Pre-training 2020-10-29 |
코드 1 |
| SepMamba + DM (S) |
- SI-SDRi
- 21.2
- SDRi
- 21.4
|
SepMamba: State-space models for speaker separation using Mamba 2024-10-28 |
코드 1 |
| TD-Conformer (XL) + DM |
- SI-SDRi
- 21.2
|
On Time Domain Conformer Models for Monaural Speech Separation in Noisy Reverberant Acoustic Environments 2023-10-09 |
코드 1 |
| Sandglasset |
- SI-SDRi
- 21.0
|
Sandglasset: A Light Multi-Granularity Self-attentive Network For Time-Domain Speech Separation 2021-03-01 |
코드 1 · 코드 2 |
| GALR |
- SI-SDRi
- 20.3
|
Effective Low-Cost Time-Domain Audio Separation Using Globally Attentive Locally Recurrent Networks 2021-01-13 |
코드 1 · 코드 2 |
| DPTNet |
- SI-SDRi
- 20.2
|
Dual-Path Transformer Network: Direct Context-Aware Modeling for End-to-End Monaural Speech Separation 2020-07-28 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 |
| Gated DualPathRNN |
- SI-SDRi
- 20.12
|
Voice Separation with an Unknown Number of Multiple Speakers 2020-02-29 |
코드 1 · 코드 2 · 코드 3 · 코드 4 |
| Sudo rm -rf (U=36) |
- SI-SDRi
- 19.5
|
Compute and memory efficient universal sound source separation 2021-03-03 |
코드 1 · 코드 2 · 코드 3 |
| Wavesplit v1 |
- SI-SDRi
- 19.0
|
Wavesplit: End-to-End Speech Separation by Speaker Clustering 2020-02-20 |
연결 없음 |
| Sudo rm -rf XL |
- SI-SDRi
- 18.9
|
Sudo rm -rf: Efficient Networks for Universal Audio Source Separation 2020-07-14 |
코드 1 · 코드 2 · 코드 3 · 코드 4 |
| Dual-path RNN |
- SI-SDRi
- 18.8
|
Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation 2019-10-14 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 · 코드 8 |
| DeepCASA |
- SI-SDRi
- 17.7
|
Divide and Conquer: A Deep CASA Approach to Talker-independent Monaural Speaker Separation 2019-04-25 |
코드 1 |