REVIEW 4 cited by
CMGAN: Conformer-based Metric GAN for Speech Enhancement
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Recently, convolution-augmented transformer (Conformer) has achieved promising performance in automatic speech recognition (ASR) and time-domain speech enhancement (SE), as it can capture both local and global dependencies in the speech signal. In this paper, we propose a conformer-based metric generative adversarial network (CMGAN) for SE in the time-frequency (TF) domain. In the generator, we utilize two-stage conformer blocks to aggregate all magnitude and complex spectrogram information by modeling both time and frequency dependencies. The estimation of magnitude and complex spectrogram is decoupled in the decoder stage and then jointly incorporated to reconstruct the enhanced speech. In addition, a metric discriminator is employed to further improve the quality of the enhanced estimated speech by optimizing the generator with respect to a corresponding evaluation score. Quantitative analysis on Voice Bank+DEMAND dataset indicates the capability of CMGAN in outperforming various previous models with a margin, i.e., PESQ of 3.41 and SSNR of 11.10 dB.
Forward citations
Cited by 4 Pith papers
-
LLM-Guided Reinforcement Learning for Audio-Visual Speech Enhancement
Using LLM-generated text descriptions of enhanced speech converted to sentiment scores as PPO rewards improves PESQ, STOI, and neural quality scores over supervised and DNSMOS-reward baselines on AVSEC-4.
-
Affine Modulation-based Audiogram Fusion Network for Joint Noise Reduction and Hearing Loss Compensation
A hearing-aid network that injects the user's audiogram into a speech-enhancement model with affine modulation beats existing joint noise-reduction and compensation systems on objective quality metrics.
-
SaD: A Scenario-Aware Discriminator for Speech Enhancement
A scenario-aware discriminator that predicts a frequency division point and scores high/low bands separately improves GAN-based speech enhancement on several quality metrics, with some STOI declines.
-
Active Speech Enhancement: Active Speech Denoising Decliping and Deveraberation
A Transformer-Mamba model that adds a learned correction signal to degraded speech beats adapted active-noise-control baselines on denoising, dereverberation, and declipping in simulation.
Discussion (0). Sign in to comment.