标题： 基于带分裂RNN的高保真语音增强 显示英文标题

标题： High Fidelity Speech Enhancement with Band-split RNN

摘要： 尽管语音增强（SE）研究取得了快速进展，但在强噪声和干扰说话人环境下提升所需语音质量仍然具有挑战性。 本文将最近提出的带分裂循环神经网络（BSRNN）模型扩展到全频段SE和个性化SE（PSE）任务。 为了减轻全频段语音中不稳定高频分量的影响，我们分别对低频子带和高频子带进行双向和单向的带级建模。 对于PSE任务，我们在BSRNN中引入说话人注册模块以利用目标说话人信息。 此外，我们利用MetricGAN判别器（MGD）和多分辨率光谱图判别器（MRSD）来改善感知质量指标。 实验结果显示，我们的系统优于多种顶级SE系统，在DNS-2020测试集上达到了最先进的（SOTA）结果，并在DNS-2023挑战赛中排名前三。 显示英文摘要

摘要： Despite the rapid progress in speech enhancement (SE) research, enhancing the quality of desired speech in environments with strong noise and interfering speakers remains challenging. In this paper, we extend the application of the recently proposed band-split RNN (BSRNN) model to full-band SE and personalized SE (PSE) tasks. To mitigate the effects of unstable high-frequency components in full-band speech, we perform bi-directional and uni-directional band-level modeling to low-frequency and high-frequency subbands, respectively. For PSE task, we incorporate a speaker enrollment module into BSRNN to utilize target speaker information. Moreover, we utilize a MetricGAN discriminator (MGD) and a multi-resolution spectrogram discriminator (MRSD) to improve perceptual quality metrics. Experimental results show that our system outperforms various top-ranking SE systems, achieves state-of-the-art (SOTA) results on the DNS-2020 test set and ranks among the top 3 in the DNS-2023 challenge.