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Audio Segmentation, Classification, and Retrieval

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Presentation on theme: "Audio Segmentation, Classification, and Retrieval"— Presentation transcript:

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

5 Smart Sound Editor Automatic Segmentation Music Speech Male Female

6 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)

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


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