HyperAI초신경

Speech Enhancement On Demand

평가 지표

PESQ (wb)
Para. (M)

평가 결과

이 벤치마크에서 각 모델의 성능 결과

비교 표
모델 이름PESQ (wb)Para. (M)
monaural-speech-enhancement-with-complex3.430.86
deepfilternet-perceptually-motivated-real3.17-
let-ssms-be-convnets-state-space-modeling3.25-
perceptual-contrast-stretching-on-target3.35-
metricgan-okd-multi-metric-optimization-of3.241.89
an-analysis-of-the-variance-of-diffusion3.11-
deep-residual-dense-lattice-network-for2.93-
the-pesqetarian-on-the-relevance-of-goodhart3.8230
모델 93.612.04
manner-multi-view-attention-network-for-noise3.21-
dense-tsnet-dense-connected-two-stage3.050.014
an-investigation-of-incorporating-mamba-for3.692.25
rose-a-recognition-oriented-speech3.0136.98
xlstm-senet-xlstm-for-single-channel-speech3.532.27
scp-gan-self-correcting-discriminator3.52-
raw-speech-enhancement-with-deep-state-space3.27-
speech-enhancement-and-dereverberation-with2.93-
a-modulation-domain-loss-for-neural-network-12.82-
모델 193.632.04
multi-view-attention-transfer-for-efficient3.121.38
metricgan-okd-multi-metric-optimization-of3.120.82
deep-residual-dense-lattice-network-for3.02-
모델 233.54-
metricgan-an-improved-version-of-metricgan3.15-
real-time-speech-enhancement-in-the-waveform3.07-
cmgan-conformer-based-metric-gan-for-monaural3.41-
d2net-a-denoising-and-dereverberation-network3.27-
metricgan-generative-adversarial-networks2.86-
deep-residual-dense-lattice-network-for2.94-
explicit-estimation-of-magnitude-and-phase3.602.26
end-to-end-speech-enhancement-based-on2.7-
investigating-training-objectives-for3.70-
deep-residual-dense-lattice-network-for2.84-
mamba-seunet-mamba-unet-for-monaural-speech3.736.28
improving-perceptual-quality-by-phone3.15-
real-time-speech-enhancement-in-the-waveform2.93-
fspen-an-ultra-lightweight-network-for-real2.970.079
primek-net-multi-scale-spectral-learning-via3.611.41
perceptual-loss-based-speech-denoising-with3.17-
boosting-self-supervised-embeddings-for3.20-