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Audio Segmentation, Classification, and Retrieval

Audio Segmentation, Classification, and Retrieval. Princeton Sound Lab Prof. Perry Cook George Tzanetakis, PhD ‘02 (CMU) Ari Lazier, ‘03 Ge Wang, G3 Tom Briggs, G2. Roadmap. Framework MARSYAS Demos: Smart Sound Editor Musical Genre Classification Content-based Query. Audio Framework.

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Audio Segmentation, Classification, and Retrieval

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  1. Audio Segmentation, Classification,and Retrieval Princeton Sound Lab Prof. Perry Cook George Tzanetakis, PhD ‘02 (CMU) Ari Lazier, ‘03 Ge Wang, G3 Tom Briggs, G2

  2. Roadmap • Framework • MARSYAS • Demos: • Smart Sound Editor • Musical Genre Classification • Content-based Query

  3. Audio Framework • MARSYAS (Tzanetakis, Cook, Lazier) • Feature Extraction • Source Segmentation • Content-based Retrieval • Classification • General Approach / Not Domain-specific • Highly Extensible

  4. Smart Sound Editor • Automatic Segmentation • Music • Speech • Male • Female • …

  5. Music Genre Classifiction • Training set: Large corpus of music and speech • How good? • 90% speech vs. music • 67% correct forced decision on genre (same agreement as humans)

  6. Content-based Query • Distance in Multi-Dimensional Feature-space • Navigate Feature-space • Nearest Neighbor / Similarity Retrieval

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