SGER: A Music Search Engine Based on Aesthetic Similarity

manaris, Bill z .查尔斯顿学院基于美学相似性的音乐搜索引擎这个SGER项目旨在开发一个基于识别美学相似性的原型音乐搜索引擎。该引擎将利用幂律度量来提取音乐理论和音乐作品的其他属性的统计比例（例如，音高，持续时间，音高距离，持续时间距离，旋律间隔，谐波间隔，旋律双谱等）。引擎使用均方误差（MSE）方法搜索与输入部分在美学上相似的部分。使用古典音乐档案语料库（14,695 MIDI作品）进行了初步测试，并结合了来自其他风格（例如爵士，摇滚，乡村等）的500多个MIDI作品。类似的度量标准已经在文本材料的美学属性上得到了验证。文本结果（作者归属、风格识别和愉快程度预测）表明了高水平的准确性。将进行评估和验证实验，以比较计算结果，表明检索件的美学相似性。这些实验将由Dwight Krehbiel教授（Bethel学院次级奖励）进行，他是认知神经科学和音乐心理学的专家，在测量音乐的情绪和生理反应方面有着丰富的经验。

AbstractIIS - 0736480Manaris, Bill Z.College of CharlestonA Music Search Engine based on Aesthetic SimilarityThis SGER project aims to develop a prototype music search engine based on identifying aesthetic similarities. This engine will utilize power-law metrics to extract statistical proportions of music-theoretic and other attributes of music pieces (e.g., Pitch, Duration, Pitch Distance, Duration Distance, Melodic Intervals, Harmonic Intervals, Melodic Bigrams, etc.).The engine searches for pieces that are aesthetically similar to the input piece using a mean squared error (MSE) approach. Preliminary testing has been done using the Classical Music Archives corpus (14,695 MIDI pieces), combined with 500+ MIDI pieces from other styles (e.g. Jazz, Rock, Country, etc.). Similar metrics have already been validated on aesthetic attributes of textual materials. Text results (author attribution, style identification, and pleasantness prediction) indicated an high level of accuracy. Assessment and validation experiments will be conducted to compare to computational findings indicating aesthetic similarity of retrieved pieces. These experiments will be conducted by Prof. Dwight Krehbiel (subaward, Bethel College), a specialist in cognitive neuroscience and psychology of music, who has extensive experience in measuring emotional and physiological responses to music.