论文标题
语音质量的机械分类
Mechanical classification of voice quality
论文作者
论文摘要
尽管没有关于良好唱歌声音的先验定义，但我们倾向于几乎可以立即对唱歌的质量进行一致的评估。这样的瞬时评估可能基于可以在短时间内感知的声音频谱。在这里，我们设计了一种贝叶斯算法，该算法学会了使用歌声的声音来评估单个歌手的合唱水平，音乐规模和性别。特别是，分类是在一组声谱强度上执行的，其频率是通过最小化贝叶斯风险来选择的。这种优化允许算法捕获对每个歧视任务至关重要的声音频率，从而产生良好的评估性能。实验结果表明，声音持续时间约为0.1秒，足以确定歌手的合唱水平和性别。通过在该算法上构建的程序，每个人都可以评估他人的合唱声音并进行私人声乐练习。
While there is no a priori definition of good singing voices, we tend to make consistent evaluations of the quality of singing almost instantaneously. Such an instantaneous evaluation might be based on the sound spectrum that can be perceived in a short time. Here we devise a Bayesian algorithm that learns to evaluate the choral proficiency, musical scale, and gender of individual singers using the sound spectra of singing voices. In particular, the classification is performed on a set of sound spectral intensities, whose frequencies are selected by minimizing the Bayes risk. This optimization allows the algorithm to capture sound frequencies that are essential for each discrimination task, resulting in a good assessment performance. Experimental results revealed that a sound duration of about 0.1 sec is sufficient for determining the choral proficiency and gender of a singer. With a program constructed on this algorithm, everyone can evaluate choral voices of others and perform private vocal exercises.
