In this paper, I have implemented a timbre visualization system called Tailors. Through the experiment with 27 MIR users, Tailors was found to be effective in conveying timbral warmth, brightness, depth, shallowness, hardness, roughness, and sharpness features of music compared to the only music condition and basic visualization. All scores of Tailors in the music imagery and music entertainment surveys were valued highest among the three conditions. Multiple linear regression analysis between timbre-imagery and imagery-entertainment shows significant and positive correlations. Coefficients comparing results from Fisher Transformation show that Tailors made user's music entertainment better through improved music visual imagery. The post-survey result represents that Tailors ranked first for the best timbre expression, music experience, and willingness to use it again. While some users felt a burden in the eye, Tailors left the future work of the data-driven approach of the mapping rule of timbre visualization to gain consent from many users. Furthermore, reducing timbre features to focus on features that Tailors can express well was also discussed, with future work of Tailors in a more artistic way using the sense of space.

翻译：本文实现了一个名为Tailors的音色可视化系统。通过与27名音乐信息检索（MIR）用户的实验发现，相较于纯音乐条件和基础可视化条件，Tailors能有效传递音乐的温暖感、明亮度、深度、浅薄感、硬度、粗糙度和尖锐度特征。在音乐意象与音乐娱乐性调查中，Tailors在所有三个条件下的评分均为最高。音色-意象与意象-娱乐性之间的多元线性回归分析显示出显著正相关。基于Fisher变换的系数比较表明，Tailors通过改善音乐视觉意象提升了用户的音乐娱乐体验。后续调查结果显示，Tailors在最佳音色表达、音乐体验及再次使用意愿三个维度均排名第一。尽管部分用户反馈存在视觉负担，Tailors为音色可视化映射规则的数据驱动方法提供了未来研究方向，以期获得更多用户认可。此外，本文还探讨了精简音色特征以聚焦Tailors擅长表达的属性，并展望了利用空间感知以更具艺术性的方式应用Tailors的未来工作。