Multi-band multi-scale DenseNet with dilated convolution for background music separation

IF 0.2 Q4 ACOUSTICS
Woon-Haeng Heo, Hyemi Kim, O. Kwon
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引用次数: 0

Abstract

We propose a multi-band multi-scale DenseNet with dilated convolution that separates background music signals from broadcast content. Dilated convolution can learn the multi-scale context information represented by spectrogram. In computer simulation experiments, the proposed architecture is shown to improve Signal to Distortion Ratio (SDR) by 0.15 dB and 0.27 dB in 0dB and –10 dB Signal to Noise Ratio (SNR) environments, respectively.
用于背景音乐分离的具有扩张卷积的多波段多尺度DenseNet
我们提出了一种多频带多尺度的扩展卷积DenseNet,用于从广播内容中分离背景音乐信号。展开卷积可以学习由谱图表示的多尺度上下文信息。在计算机仿真实验中,该结构在0dB和-10 dB信噪比(SNR)环境下分别提高了0.15 dB和0.27 dB的信失真比(SDR)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
0.60
自引率
50.00%
发文量
1
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