CMGAN：基于Conformer的度量生成对抗网络用于语音增强
CMGAN: Conformer-based Metric GAN for Speech Enhancement
AI总结：
本文提出CMGAN，在时频域利用两阶段Conformer块聚合幅度与复数频谱信息，并结合度量判别器优化生成器，在Voice Bank+DEMAND数据集上取得PESQ 3.41和SSNR 11.10 dB的领先语音增强性能。
摘要：
近年来，卷积增强Transformer（Conformer）在自动语音识别（ASR）和时域语音增强（SE）中取得了令人瞩目的性能，因为它能够同时捕捉语音信号中的局部和全局依赖关系。本文提出了一种基于Conformer的度量生成对抗网络（CMGAN），用于时频（TF）域的语音增强。在生成器中，我们利用两阶段Conformer块，通过对时间和频率依赖关系进行建模，聚合所有幅度和复数频谱信息。幅度和复数频谱的估计在解码器阶段被解耦，然后联合合并以重建增强后的语音。此外，采用度量判别器，通过针对相应评估分数优化生成器，进一步提高增强估计语音的质量。在Voice Bank+DEMAND数据集上的定量分析表明，CMGAN能够以明显优势超越多种先前模型，即PESQ为3.41，SSNR为11.10 dB。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
