混响音乐中音乐信号的掩蔽与降噪处理

IF 2.1 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Intelligent Systems Pub Date : 2022-01-01 DOI:10.1515/jisys-2022-0024
Shenghuan Zhang, Ye Cheng
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引用次数: 0

摘要

在录音过程中,噪声不可避免地混入音乐信号中。为了提高音乐信号的质量,必须尽可能地降低噪声。本文简要介绍了混响音乐中的噪声、掩蔽效应以及降低噪声的频谱减法。利用人耳掩蔽效应对谱减法进行改进,提高了谱减法的降噪性能。对传统的和改进的谱减法进行了仿真实验。结果表明,改进的谱减法能更有效地降低混响音乐中的噪声;在信噪比这一客观评价标准下,改进谱减法处理后的消混响音乐信号具有较高的信噪比;在主观评价标准平均意见评分(MOS)下,改进谱减法处理后的消混响音乐信号也有较好的评价。
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Masking and noise reduction processing of music signals in reverberant music
Abstract Noise will be inevitably mixed with music signals in the recording process. To improve the quality of music signals, it is necessary to reduce noise as much as possible. This article briefly introduces noise, the masking effect, and the spectral subtraction method for reducing noise in reverberant music. The spectral subtraction method was improved by the human ear masking effect to enhance its noise reduction performance. Simulation experiments were carried out on the traditional and improved spectral subtraction methods. The results showed that the improved spectral subtraction method could reduce the noise in reverberant music more effectively; under an objective evaluation criterion, the signal-to-noise ratio, the de-reverberated music signal processed by the improved spectral subtraction method had a higher signal-to-noise ratio; under a subjective evaluation criterion, mean opinion score (MOS), the de-reverberated music signal processed by the improved spectral subtraction method also had a better evaluation.
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来源期刊
Journal of Intelligent Systems
Journal of Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
3.30%
发文量
77
审稿时长
51 weeks
期刊介绍: The Journal of Intelligent Systems aims to provide research and review papers, as well as Brief Communications at an interdisciplinary level, with the field of intelligent systems providing the focal point. This field includes areas like artificial intelligence, models and computational theories of human cognition, perception and motivation; brain models, artificial neural nets and neural computing. It covers contributions from the social, human and computer sciences to the analysis and application of information technology.
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