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| Content Provider | ACM Digital Library |
|---|---|
| Author | Smith, Benjamin Garnett, Guy |
| Abstract | Recent developments in machine listening present opportunities for innovative new paradigms for computer-human interaction. Voice recognition systems demonstrate a typical approach that conforms to event oriented control models. However, acoustic sound is continuous, and highly dimensional, presenting a rich medium for computer interaction. Unsupervised machine learning models present great potential for real-time machine listening and understanding of audio and sound data. We propose a method for harnessing unsupervised machine learning algorithms, Adaptive Resonance Theory specifically, in order to inform machine listening, build musical context information, and drive real-time interactive performance systems. We present the design and evaluation of this model leveraging the expertise of trained, improvising musicians. |
| Starting Page | 293 |
| Ending Page | 296 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450310482 |
| DOI | 10.1145/2166966.2167021 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-02-14 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Unsupervised machine learning Adaptive resonance theory Music Machine listening Artificial intelligence |
| Content Type | Text |
| Resource Type | Article |
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