EAGER: An Efficient Algorithm for Automated Transcription of Music, Vocalizations, and Arbitrary Sound Recordings

EAGER：一种用于自动转录音乐、发声和任意录音的高效算法

• 批准号: 1049554
• 负责人: Bill Manaris
• 金额: $ 3.7万
• 依托单位: College of Charleston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1049554&HistoricalAwards=false
• 关键词: EAGER; Efficient; Algorithm; Automated; Transcription

AbstractThis proposed EAGER project focuses on a new, robust and efficient technique to transcribe arbitrary sounds. It also extends in a novel and transformative fashion earlier work that developed a music search engine based on identifying aesthetic similarities. Automated transcription of musical sounds is still an open research area and one of exceptional importance. While some limited transcription techniques are available, they lack critical abilities, such as inability to transcribe polyphonic compositions or difficulties in distinguishing sounds produced by different sources. In addition, the frequency ranges are limited, thereby excluding a vast number of musical works. The approach proposed in this project involves an innovative audio-to-MIDI transcription algorithm, which handles polyphonic compositions, captures harmonic, vocal and percussive instrumentation, is very efficient and works with sounds beyond human produced musical compositions, such as bird songs and sub/ultrasonic animal vocalizations.

摘要：这个提议的 EAGER 项目专注于一种新的、强大的、高效的技术来转录任意声音。 它还以新颖且变革性的方式扩展了早期的工作，该工作开发了基于识别审美相似性的音乐搜索引擎。 音乐声音的自动转录仍然是一个开放的研究领域，也是极其重要的领域之一。 虽然有一些有限的转录技术可用，但它们缺乏关键能力，例如无法转录复调作品或难以区分不同来源产生的声音。 此外，频率范围有限，因此排除了大量音乐作品。 该项目提出的方法涉及一种创新的音频到 MIDI 转录算法，该算法可以处理复调作品，捕获和声、声乐和打击乐器，非常高效，并且可以处理人类创作的音乐作品之外的声音，例如鸟鸣和亚音速/超声波动物发声。