EAGER: Automatic Indexing of Polyphonic Music by Cascade Classifiers

IIS - 0968647使用级联分类器自动索引复调音乐。 北卡罗来纳州夏洛特大学摘要EAGER项目的目标是支持一类新的级联分类器和混合分类器的探索性工作，用于根据乐器和乐器类型自动索引复调音乐。测试用于复调音乐的自动索引的新分类器，特别是那些用于从管弦乐录音中自动分类乐器声音的分类器是困难的，并且涉及高度的风险和结果的不确定性。如果成功，结果可能会被证明是变革性的，并对音乐信息分析产生重大影响。 这项工作将利用早期NSF支持项目中开发的米拉伊数据库中的资源。 主米拉伊数据库包含约1，000，000种乐器声音，每种声音表示为约1，000个特征的向量。 每个乐器的声音都被识别并与相应的乐器相匹配。

IIS - 0968647 Automatic Indexing of Polyphonic Music by Cascade ClassifiersRas, Zbigniew W. University of North Carolina at CharlotteAbstractThe goal of this EAGER project is to support exploratory work on a new class of cascade classifiers and hybrid classifiers for automatic indexing of polyphonic music according to instruments and types of instruments. Testing new classifiers for automatic indexing of polyphonic music, specifically those for the automatic classification of instrumental sound from recordings of orchestral music is difficult and involves a high degree of risk and uncertainty as to the outcome. If successful, the results may prove to be transformative and have significant impact on music information analysis. The work will employ resources in the MIRAI database developed in an earlier NSF supported project. The main MIRAI database contains about 1,000,000 musical instrument sounds, each represented as a vector of approximately 1,000 features. Each instrument sound is identified and matched to a corresponding instrument.