SBIR Phase I: Sound-object Recognition for Real-time or Offline Systems

这个小型企业创新研究第一阶段项目将研究声音对象识别算法，供专业和消费者录音和现场音响工程师使用。还将开发用于强大的离线乐器识别，音乐循环检索，对话/音效/音乐识别和飞行机器收听的算法。音乐家和音频工程师可以访问数千兆字节的音频内容，但查找音频内容的最新技术是通过文本查询和导航静态文件层次结构。目前，没有音频软件制造商提供用于通过其音频内容搜索音频循环的工具。此外，录音和现场声音工程师有复杂的组织和导航职责，这可以使用实时音频分析算法来解决。如果成功的话，这一努力将能够使用自上而下的方法识别音频内容-使用一系列分层机器学习分类器，根据从最大的真实世界音频内容集合中提取的统计特征进行训练。开发的离线机器分类器将被移植到实时的时间，嵌入式机器收听算法，并用于增强传统的音频信号处理工具。 此外，这一努力将促进工业界和学术界之间的互动和合作。鼓励赞助的研究协议，来自行业工程师的客座讲师，以及直接专注于解决应用，行业挑战的课程。该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。

This Small Business Innovation Research Phase I project will research sound-object recognition algorithms for use by professional and consumer audio recording and live sound engineers. Algorithms for robust off-line instrument recognition, music loop retrieval, dialog/sound effect/music recognition, and on-the-fly machine listening will also be developed. Musicians and audio engineers have access to gigabytes of audio content yet, the state of the art for finding audio content is through text queries and navigating static file hierarchies. Currently, none of the audio software manufacturers provide tools for searching for audio loops by their audio content. Additionally, recording and live sound engineers have complex organization and navigation duties, which could be solved using real-time audio analysis algorithms. If successful, this effort will enable recognizing audio content using a top-down approach - using a fleet of hierarchical machine learning classifiers, trained on statistical features extracted from one of the largest real-world audio content collections. Developed off-line machine classifiers will be ported to real-time time, embedded machine-listening algorithms, and used to enhance traditional audio signal processing tools. Further, the effort will foster interaction and collaboration between industry and academia ? encouraging sponsored research agreements, guest lecturers from industry engineers, and courses which directly focus on solving applied, industry challenges.This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).