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

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).