Classification of phonation types in singing voice using wavelet scattering network-based features.

IF 1.2 Q3 ACOUSTICS
Kiran Reddy Mittapalle, Paavo Alku
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引用次数: 0

Abstract

The automatic classification of phonation types in singing voice is essential for tasks such as identification of singing style. In this study, it is proposed to use wavelet scattering network (WSN)-based features for classification of phonation types in singing voice. WSN, which has a close similarity with auditory physiological models, generates acoustic features that greatly characterize the information related to pitch, formants, and timbre. Hence, the WSN-based features can effectively capture the discriminative information across phonation types in singing voice. The experimental results show that the proposed WSN-based features improved phonation classification accuracy by at least 9% compared to state-of-the-art features.

利用基于小波散射网络的特征对歌声中的发音类型进行分类。
对歌声中的发音类型进行自动分类对于识别歌声风格等任务至关重要。本研究建议使用基于小波散射网络(WSN)的特征对歌唱声音的发音类型进行分类。小波散射网络(WSN)与听觉生理模型十分相似,它生成的声学特征能极大地表征与音高、声母和音色相关的信息。因此,基于 WSN 的特征能有效捕捉歌声中不同发音类型的辨别信息。实验结果表明,与最先进的特征相比,基于 WSN 特征的语音分类准确率至少提高了 9%。
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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CiteScore
1.70
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0.00%
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