论文标题
移动音频流网络有效的低延迟语音增强
Efficient Low-Latency Speech Enhancement with Mobile Audio Streaming Networks
论文作者
论文摘要
我们建议移动音频流网络（MASNET），以提高有效的低延迟语音，这特别适用于移动设备和其他计算能力是限制的应用程序。 MASNET处理线性尺度频谱图，将连续的嘈杂框架转化为复杂值掩模，然后将其应用于相应的嘈杂框架。 MASNET可以在低延迟的增量推理模式下进行操作，该模式与层面批处理模式的复杂性相匹配。与类似的全趋验结构相比，MasNet融合了深度和尖锐的卷积，以大幅度减少融合的多重蓄电池每秒（FMA/s），而SNR的成本有所减少。
We propose Mobile Audio Streaming Networks (MASnet) for efficient low-latency speech enhancement, which is particularly suitable for mobile devices and other applications where computational capacity is a limitation. MASnet processes linear-scale spectrograms, transforming successive noisy frames into complex-valued ratio masks which are then applied to the respective noisy frames. MASnet can operate in a low-latency incremental inference mode which matches the complexity of layer-by-layer batch mode. Compared to a similar fully-convolutional architecture, MASnet incorporates depthwise and pointwise convolutions for a large reduction in fused multiply-accumulate operations per second (FMA/s), at the cost of some reduction in SNR.
