MUSART: Music Retrieval Via Aural Queries

MUSART is a research project developing and studying new techniques for music information retrieval. The MUSART architecture uses a variety of representations to support multiple search modes. Progress is reported on the use of Markov modeling, melodic contour, and phonetic streams for music retrieval. To enable large-scale databases and more advanced searches, musical abstraction is studied. The MME subsystem performs theme extraction, and two other analysis systems are described that discover structure in audio representations of music. Theme extraction and structure analysis promise to improve search quality and support better browsing and “audio thumbnailing.” Integration of these components within a single architecture will enable scientific comparison of different techniques and, ultimately, their use in combination for improved performance and functionality.

[1]  Roger B. Dannenberg,et al.  Melody Matching Directly From Audio , 2001 .

[2]  William P. Birmingham,et al.  Automated Partitioning of Tonal Music , 2000, FLAIRS.

[3]  Ian H. Witten,et al.  Towards the digital music library: tune retrieval from acoustic input , 1996, DL '96.

[4]  G. H. Wakefield,et al.  To catch a chorus: using chroma-based representations for audio thumbnailing , 2001, Proceedings of the 2001 IEEE Workshop on the Applications of Signal Processing to Audio and Acoustics (Cat. No.01TH8575).

[5]  Andreas Kornstädt,et al.  Themefinder: A web-based melodic search tool , 1998 .

[6]  Joseph Rothstein,et al.  MIDI: A Comprehensive Introduction , 1992 .

[7]  Colin Meek,et al.  Thematic Extractor , 2001, ISMIR.

[8]  Jonathan Foote,et al.  Visualizing music and audio using self-similarity , 1999, MULTIMEDIA '99.

[9]  Eleanor Selfridge-Field,et al.  Melodic Similarity : concepts, procedures, and applications , 1998 .

[10]  David Bainbridge The role of Music IR in the New Zealand Digital Music Library project , 2000, ISMIR.

[11]  Yuen-Hsien Tseng,et al.  Content-based retrieval for music collections , 1999, SIGIR '99.

[12]  Gregory H. Wakefield,et al.  Mathematical representation of joint time-chroma distributions , 1999, Optics & Photonics.

[13]  Jean-Gabriel Ganascia,et al.  Musical content-based retrieval: an overview of the Melodiscov approach and system , 1999, MULTIMEDIA '99.

[14]  Jon W. Dunn,et al.  VARIATIONS: a digital music library system at Indiana University , 1999, DL '99.

[15]  Jonathan Foote,et al.  Automatic audio segmentation using a measure of audio novelty , 2000, 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No.00TH8532).

[16]  R. Shepard Circularity in Judgments of Relative Pitch , 1964 .

[17]  C.-C. Jay Kuo,et al.  Hierarchical system for content-based audio classification and retrieval , 1998, Other Conferences.

[18]  William Forde Thompson Melodic Similarity: Concepts, Procedures, and Applications (Computing in Musicology 11) Walter B. Hewlett Eleanor Selfridge-Field , 1999 .

[19]  Ian H. Witten,et al.  The New Zealand Digital Library MELody inDEX , 1997, D Lib Mag..

[20]  Beth Logan,et al.  Music summarization using key phrases , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[21]  Shane S. Sturrock,et al.  Time Warps, String Edits, and Macromolecules – The Theory and Practice of Sequence Comparison . David Sankoff and Joseph Kruskal. ISBN 1-57586-217-4. Price £13.95 (US$22·95). , 2000 .

[22]  Thom Blum,et al.  Audio databases with content-based retrieval , 1997 .