论文标题
基于谐波的单簧管音调质量评估的表示
Harmonics Based Representation in Clarinet Tone Quality Evaluation
论文作者
论文摘要
音乐音质质量评估通常由专家执行。它可能是主观的，缺乏一致性和公平性以及耗时的。在本文中，我们提出了一种通过基于谐波结构和能量分布来评估音调质量来识别单簧管芦苇质量的新方法。我们首先将基于声学谐波的芦苇和单簧管管的质量分离，并发现芦苇质量与谐波的均匀部分密切相关。然后，我们构建了一个由均匀的谐波包膜和频谱中谐波的能量分布组成的特征集。带注释的单簧管音频数据记录在3个级别的表演者中，并且通过机器学习对音调质量进行了分类。结果表明，我们识别低音调和中等高音的新方法显着优于先前的方法。
Music tone quality evaluation is generally performed by experts. It could be subjective and short of consistency and fairness as well as time-consuming. In this paper we present a new method for identifying the clarinet reed quality by evaluating tone quality based on the harmonic structure and energy distribution. We first decouple the quality of reed and clarinet pipe based on the acoustic harmonics, and discover that the reed quality is strongly relevant to the even parts of the harmonics. Then we construct a features set consisting of the even harmonic envelope and the energy distribution of harmonics in spectrum. The annotated clarinet audio data are recorded from 3 levels of performers and the tone quality is classified by machine learning. The results show that our new method for identifying low and medium high tones significantly outperforms previous methods.
