首页> 外文期刊>Journal of voice: official journal of the Voice Foundation >Classification of the classical male singing voice using long-term average spectrum.
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Classification of the classical male singing voice using long-term average spectrum.

机译:使用长期平均频谱对古典男性歌声进行分类。

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OBJECTIVES/HYPOTHESIS: Singing-voice classification is often considered the cornerstone of a classical singer's identity. Traditionally, classification has been a highly subjective, nonstandardized process. As a result, misclassification of the singing voice is thought to be common, especially in young singers. Long-term average spectrum (LTAS) average is an objective measurement that could be used to classify a singer's voice. The purpose of this study was to determine the relationship of LTAS with singing-voice classification. STUDY DESIGN: Descriptive between-subject study. METHODS: Nine professional classical male singers performed the "Star Spangled Banner" in a comfortable key of their choice. LTAS was calculated for the first two phrases, the remainder of the song, and the entire song. The overall LTAS averages of each sample as well as the physiological and singing ranges were compared with self-reported singing-voice classification. RESULTS: Voice classification and overall LTAS average were moderately correlated, but the strength of the correlation varied with each sample. The strongest correlation was with the entire song. Voice classification and singing range were strongly correlated. CONCLUSIONS: LTAS remains a promising tool to aid in singing-voice classification. However, how to best use LTAS in classification remains unclear because of the influence of sample length and phonetic and pitch content on LTAS.
机译:目标/假设:歌声分类通常被认为是古典歌手身份的基石。传统上,分类是一个高度主观的,非标准化的过程。结果,唱歌声的错误分类被认为是普遍的,尤其是在年轻歌手中。长期平均频谱(LTAS)平均值是一种客观的度量,可以用来对歌手的声音进行分类。这项研究的目的是确定LTAS与歌声分类的关系。研究设计:描述性学科间研究。方法:9名专业古典男歌手以他们选择的舒适琴键演奏了“星条旗”。 LTAS是针对前两个短语,歌曲的其余部分以及整首歌曲计算的。将每个样本的总体LTAS平均值以及生理和歌唱范围与自我报告的歌唱声音分类进行比较。结果:语音分类和总体LTAS平均值具有中等程度的相关性,但相关强度随每个样本而异。与整个歌曲的相关性最强。声音分类和歌唱范围紧密相关。结论:LTAS仍然是一个有前途的工具,可以帮助歌声分类。但是,由于样本长度以及语音和音高含量对LTAS的影响,如何在分类中最好地使用LTAS尚不清楚。

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