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检测小提琴声学特性变化的感知阈值。

Perceptual thresholds for detecting modifications applied to the acoustical properties of a violin.

作者信息

Fritz Claudia, Cross Ian, Moore Brian C J, Woodhouse Jim

机构信息

Centre for Music and Science, Music Faculty, University of Cambridge, West Road, Cambridge CB3 9DP, United Kingdom.

出版信息

J Acoust Soc Am. 2007 Dec;122(6):3640-50. doi: 10.1121/1.2799475.

Abstract

This study is the first step in the psychoacoustic exploration of perceptual differences between the sounds of different violins. A method was used which enabled the same performance to be replayed on different "virtual violins," so that the relationships between acoustical characteristics of violins and perceived qualities could be explored. Recordings of real performances were made using a bridge-mounted force transducer, giving an accurate representation of the signal from the violin string. These were then played through filters corresponding to the admittance curves of different violins. Initially, limits of listener performance in detecting changes in acoustical characteristics were characterized. These consisted of shifts in frequency or increases in amplitude of single modes or frequency bands that have been proposed previously to be significant in the perception of violin sound quality. Thresholds were significantly lower for musically trained than for nontrained subjects but were not significantly affected by the violin used as a baseline. Thresholds for the musicians typically ranged from 3 to 6 dB for amplitude changes and 1.5%-20% for frequency changes. Interpretation of the results using excitation patterns showed that thresholds for the best subjects were quite well predicted by a multichannel model based on optimal processing.

摘要

本研究是对不同小提琴声音感知差异进行心理声学探索的第一步。所采用的方法能使同一演奏在不同的“虚拟小提琴”上重放,从而探究小提琴声学特性与感知品质之间的关系。使用安装在琴桥上的力传感器对真实演奏进行录音,以精确呈现来自小提琴弦的信号。然后将这些信号通过与不同小提琴导纳曲线相对应的滤波器播放。最初,对听众检测声学特性变化的表现极限进行了表征。这些变化包括单模态或频段的频率偏移或幅度增加,此前已有人提出这些变化在小提琴音质感知中具有重要意义。受过音乐训练的受试者的阈值显著低于未受过训练的受试者,但不受用作基线的小提琴的显著影响。音乐家的幅度变化阈值通常在3至6分贝之间,频率变化阈值在1.5%至20%之间。使用激励模式对结果进行解释表明,基于最优处理的多通道模型能很好地预测最佳受试者的阈值。

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