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Speech Enhancement On Deep Noise Suppression

Metrics

PESQ-WB
SI-SDR-WB

Results

Performance results of various models on this benchmark

Paper Title
ZipEnhancer (M)3.8122.22-
TF-Locoformer (M)3.7223.3TF-Locoformer: Transformer with Local Modeling by Convolution for Speech Separation and Enhancement
ZipEnhancer (S)3.6921.15-
MP-SENet3.6221.03Explicit Estimation of Magnitude and Phase Spectra in Parallel for High-Quality Speech Enhancement
BSRNN-S + MRSD3.5321.4High Fidelity Speech Enhancement with Band-split RNN
BSRNN-16k3.4521.1High Fidelity Speech Enhancement with Band-split RNN
MFNET3.4320.31A Mask Free Neural Network for Monaural Speech Enhancement
BSRNN-S3.4221.3High Fidelity Speech Enhancement with Band-split RNN
BSRNN3.32-High Fidelity Speech Enhancement with Band-split RNN
CleanUNet-23.262-CleanUNet 2: A Hybrid Speech Denoising Model on Waveform and Spectrogram
FRCRN3.23-Monaural Speech Enhancement with Complex Convolutional Block Attention Module and Joint Time Frequency Losses
FullSubNet+3.21816.81FullSubNet+: Channel Attention FullSubNet with Complex Spectrograms for Speech Enhancement
CleanUNet3.146-Speech Denoising in the Waveform Domain with Self-Attention
aTENNuate2.98-aTENNuate: Optimized Real-time Speech Enhancement with Deep SSMs on Raw Audio
Sudo rm -rf (U=32)2.9519.7RemixIT: Continual self-training of speech enhancement models via bootstrapped remixing
DCTCRN-P2.82-Real-time Monaural Speech Enhancement With Short-time Discrete Cosine Transform
DCTCRN-T2.82-Real-time Monaural Speech Enhancement With Short-time Discrete Cosine Transform
DCCRN-E2.79-Real-time Monaural Speech Enhancement With Short-time Discrete Cosine Transform
PoCoNet2.7885-PoCoNet: Better Speech Enhancement with Frequency-Positional Embeddings, Semi-Supervised Conversational Data, and Biased Loss
FullSubNet2.77717.29FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement
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Speech Enhancement On Deep Noise Suppression | SOTA | HyperAI