卷积神经网络
计算机科学
编码器
卷积码
帧(网络)
语音识别
时域
人工智能
噪音(视频)
模式识别(心理学)
代表(政治)
解码方法
算法
计算机视觉
电信
操作系统
图像(数学)
政治
政治学
法学
作者
Ashutosh Pandey,DeLiang Wang
标识
DOI:10.1109/icassp.2019.8683634
摘要
This work proposes a fully convolutional neural network (CNN) for real-time speech enhancement in the time domain. The proposed CNN is an encoder-decoder based architecture with an additional temporal convolutional module (TCM) inserted between the encoder and the decoder. We call this architecture a Temporal Convolutional Neural Network (TCNN). The encoder in the TCNN creates a low dimensional representation of a noisy input frame. The TCM uses causal and dilated convolutional layers to utilize the encoder output of the current and previous frames. The decoder uses the TCM output to reconstruct the enhanced frame. The proposed model is trained in a speaker- and noise-independent way. Experimental results demonstrate that the proposed model gives consistently better enhancement results than a state-of-the-art real-time convolutional recurrent model. Moreover, since the model is fully convolutional, it has much fewer trainable parameters than earlier models.
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