基于延迟子带LSTM的在线单声道语音增强
Online Monaural Speech Enhancement Using Delayed Subband LSTM
AI总结：
针对在线单声道语音增强需求，提出跨频率复用LSTM的延迟子带网络，通过子带训练与输出延迟前瞻架构降低参数量并提升性能，在DNS数据集上优于GRU全带基线方法。
摘要：
本文提出一种用于在线单声道（单通道）语音增强的延迟子带LSTM网络。该方法在短时傅里叶变换（STFT）域中构建，在线处理要求逐帧接收并处理信号。该方法的核心特点是跨频率复用同一个LSTM，可大幅减少网络参数量、训练数据量与计算负担。训练以子带方式进行：输入由单个频率及若干上下文频率组成。网络基于信号平稳性与局部频谱模式学习语音-噪声判别函数，据此预测每个频率处的干净语音掩码。为利用未来信息即前瞻信息，本文提出一种输出延迟子带架构，使单向前向网络除当前帧外还能处理若干未来帧。我们将该方法应用于DNS实时语音增强挑战赛。基于DNS数据集的实验表明，该方法取得的性能评测分数优于采用门控循环单元网络学习全带频谱的DNS基线方法。
英文摘要：
This paper proposes a delayed subband LSTM network for online monaural (single-channel) speech enhancement. The proposed method is developed in the short time Fourier transform (STFT) domain. Online processing requires frame-by-frame signal reception and processing. A paramount feature of the proposed method is that the same LSTM is used across frequencies, which drastically reduces the number of network parameters, the amount of training data and the computational burden. Training is performed in a subband manner: the input consists of one frequency, together with a few context frequencies. The network learns a speech-to-noise discriminative function relying on the signal stationarity and on the local spectral pattern, based on which it predicts a clean-speech mask at each frequency. To exploit future information, i.e. look-ahead, we propose an output-delayed subband architecture, which allows the unidirectional forward network to process a few future frames in addition to the current frame. We leverage the proposed method to participate to the DNS real-time speech enhancement challenge. Experiments with the DNS dataset show that the proposed method achieves better performance-measuring scores than the DNS baseline method, which learns the full-band spectra using a gated recurrent unit network.
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。
