基于人工智能的音乐语音质量理论评价模型构建

Xingye Su, Hua Chen
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引用次数: 0

摘要

音乐语音质量理论评价模型的构建一直是研究的热点。音乐语音质量的好坏直接影响到测评结果的可靠性。传统的音乐发音质量分析方法主要是通过统计指标来分析音乐的发音问题,但统计指标无法对音乐的发音问题进行分析。为了提高音乐的检测质量,本文提出了一种基于人工智能技术融合的音乐发音质量评价理论模型。在满足音程约束的前提下,根据音乐的发音理论生成样本的人工智能树,并根据人工智能树对音乐的发音质量进行评价。最后,通过实验验证了该模型的有效性、高效性和可扩展性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Construction of Music Pronunciation Quality Theory Evaluation Model Based on Artificial Intelligence
The research on the construction of evaluation model of music pronunciation quality theory has always been a hot topic. Music pronunciation quality has a direct impact on the reliability of assessment results. Traditional methods of music pronunciation quality analysis mainly analyze the pronunciation problem of music through the statistical index, but the statistical index cannot In order to improve the quality of detecting music, this paper presents a theory model of evaluating the quality of music pronunciation based on the fusion of artificial intelligence technology. Under the premise of meeting the interval constraints, according to the pronunciation theory of music The sample generated artificial intelligence tree, and evaluated the quality of music pronunciation according to artificial intelligence tree. Finally, the validity, the efficiency and the scalability of the model were verified through experiments.
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