论文标题
双信号转换LSTM网络用于实时噪声抑制
Dual-Signal Transformation LSTM Network for Real-Time Noise Suppression
论文作者
论文摘要
本文介绍了双重信号转换LSTM网络（DTLN），以实时演讲，这是深度噪声抑制挑战（DNS-Challenge）的一部分。这种方法结合了短时的傅立叶变换（STFT），并以少于一百万个参数的堆叠网络方法中学习的分析和合成基础。该模型接受了挑战组织者提供的500小时嘈杂的演讲培训。该网络能够实时处理（一帧，一帧）并达到竞争成果。结合这两种类型的信号转换使DTLN可以从幅度光谱中鲁棒提取信息，并从学习特征基础中结合相位信息。该方法显示出最新的性能，并且在平均意见分数（MOS）方面，绝对超过了DNS-Challenge基线。
This paper introduces a dual-signal transformation LSTM network (DTLN) for real-time speech enhancement as part of the Deep Noise Suppression Challenge (DNS-Challenge). This approach combines a short-time Fourier transform (STFT) and a learned analysis and synthesis basis in a stacked-network approach with less than one million parameters. The model was trained on 500 h of noisy speech provided by the challenge organizers. The network is capable of real-time processing (one frame in, one frame out) and reaches competitive results. Combining these two types of signal transformations enables the DTLN to robustly extract information from magnitude spectra and incorporate phase information from the learned feature basis. The method shows state-of-the-art performance and outperforms the DNS-Challenge baseline by 0.24 points absolute in terms of the mean opinion score (MOS).
