Investigating Music Collections at Different Scales with AudioDB

References

• Abdallah , S. , Sandler , M. , Rhodes , C. and Casey , M. 2006 . Using duration models to reduce fragmentation in audio segmentation . Machine Learning , 65 : 485 – 515 .
   Google Scholar
• Andoni , A. and Indyk , P. Near-optimal hashing algorithms for near neighbor problem in high dimensions . Proceedings of the 47th Annual Symposium on Foundations of Computer Science . Berkeley, CA, USA. pp. 459 – 468 . Washington, DC : IEEE Computer Society .
   Google Scholar
• Baluja , S. and Covell , M. 2008 . Learning to hash: Forgiving hash functions and applications . Data Mining and Knowledge Discovery , 17 ( 3 ) : 402 – 430 .
   Google Scholar
• Broder , A. , Glassman , S. , Manasse , M. and Zweig , G. Syntactic clustering of the web . Selected Papers from the Sixth International Conference on World Wide Web . pp. 1157 – 1166 . Berlin : Elsevier Science .
   Google Scholar
• Byrd , D. 2007 . “ A similarity scale for content-based music IR. Retrieved from ” . http://www.informatics.indiana.edu/donbyrd/MusicSimilarityScale.HTML
   Google Scholar
• Casey , M. 2001 . MPEG-7 sound recognition tools . IEEE Transactions on Circuits and Systems Video Technology , 11 ( 6 ) : 737 – 747 . [Special issue on MPEG-7]
   Google Scholar
• Casey , M. 2009 . “ Audio tools for music discovery ” . In Modern Methods for Musicology: Prospects, Proposals and Realities , Edited by: Crawford , T. and Gibson , L. 127 – 135 . Aldershot : Ashgate . (Digital Research in the Arts and Humanities
   Google Scholar
• Casey , M. , Rhodes , C. and Slaney , M. 2008 . Analysis of minimum distances in high-dimensional musical spaces . IEEE Transactions on Audio, Speech and Signal Processing , 16 ( 5 ) : 1015 – 1028 .
   Google Scholar
• Casey , M. and Slaney , M. Song intersection by approximate nearest neighbour searching . Proceedings of the International Symposium on Music Information Retrieval (ISMIR) . Victoria, Canada. pp. 35 – 40 .
   Google Scholar
• Casey , M. and Slaney , M. The Importance of Sequences in Music Similarity . Proc. IEEE International Conference on Acoustics, Speech and Signal Processing . Toulouse, France. Vol. V , pp. 5 – 8 .
   Google Scholar
• Cook , N. 2007 . Performance analysis and Chopin's mazurkas . Musicae Scientiae , 11 ( 2 ) : 183 – 208 .
   Web of Science ®Google Scholar
• Cook , N. in preparation . “ The ghost in the machine: Towards a musicology of recordings ” . In Musicae Scientiae
   Google Scholar
• Cook , N. and Sapp , C. 2008 . “ Purely coincidental? Joyce Hatto and Chopin's Mazurkas ” . Retrieved from http://www.charm.rhul.ac.uk/content/contact/hatto_article.html
   Google Scholar
• Crawford , T. , Mauch , M. and Rhodes , C. 2010 . “ Recognising classical works in historical recordings ” . In Proceedings of the International Symposium on Music Information Retrieval Utrecht, the Netherlands, pp. 495–500
   Google Scholar
• Datar , M. , Indyk , P. , Immorlica , N. and Mirrokni , V. Locality-sensitive hashing scheme based on p-stable distributions . Proceedings of the 20th ACM Symposium on Computational Geometry . Brooklyn, New York, USA. pp. 253 – 262 . New York : Association for Computing Machinery. .
   Google Scholar
• d'Inverno , M. , Casey , M. , Rhodes , C. and Jewell , M. in preparation . “ Content-based search for time-based media ” .
   Google Scholar
• Ellis , D. 2007 . Beat tracking by dynamic programming . Journal of New Music Research , 36 ( 1 ) : 51 – 60 . [Special issue on beat and tempo extraction]
   Web of Science ®Google Scholar
• Ellis , D. and Poliner , G. Identifying ‘cover songs’ with chroma features and dynamic programming beat tracking . Proceedings of the International Conference on Acoustics, Speech, & Signal Processing ICASSP-07 . Honolulu, Hawai'i. pp. IV1429 – 1432 .
   Google Scholar
• Flexer , A. , Pampalk , E. and Widmer , G. 2005 . “ Hidden Markov models for spectral similarity of songs (Technical report). Österreichisches Forschungsinstitut für Artificial Intelligence ” .
   Google Scholar
• Foote , J. Visualizing music and audio using self-similarity . Proceedings of the 7th ACM International Conference on Multimedia . Orlando, FL. Vol. 1 , pp. 77 – 80 . New York : Association for Computing Machinery .
   Google Scholar
• Grauman , K. and Darrell , T. Fast contour matching using approximate Earth mover's distance . Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 200) . Washington : DC, Vol. I, pp. 220–227 .
   Google Scholar
• John , J. 2006 . Pandora and the music genome project . Scientific Computing , Retrieved from http://www.pandora. com/mgp.shtml
   Google Scholar
• Levy , M. and Sandler , M. Lightweight measures for timbral similarity of musical audio . Proceedings of the 1st ACM Workshop on Audio and Music Computing Multimedia . Santa Barbara, CA. pp. 27 – 36 .
   Google Scholar
• Logan , B. and Salomon , A. A music similarity function based on signal analysis . Proceedings of the IEEE International Conference on Multimedia and Expo . Tokyo, Japan. pp. 190 – 193 .
   Google Scholar
• Magas , M. , Casey , M. and Rhodes , C. 2008 . “ mHashup: Fast visual music discovery via locality sensitive hashing ” . In ACM SIGGRAPH New Technology Demos New Orleans, LA Retrieved from http://dx.doi.org/10.1145/1401615.1401641
   Google Scholar
• Magas , M. and Proutskova , P. 2009 . “ A location-tracking interface for ethnomusicological collections ” . In Workshop on Exploring Musical Information Spaces 12 – 17 . Corfu, Greece
   Google Scholar
• Mauch , M. and Dixon , S. Approximate note transcription for the improved identification of difficult chords . Proceedings of the International Symposium on Music Information Retrieval . Utrecht, the Netherlands, pp. 135–140
   Google Scholar
• Müller , M. 2007 . Information Retrieval for Music and Motion , Berlin : Springer Verlag .
   Google Scholar
• Rauber , A. , Pampalk , E. and Merkl , D. Content-based music indexing and organization . Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR02) . Tampere, Finland. pp. 409 – 410 .
   Google Scholar
• Rhodes , C. and Casey , M. Algorithms for determining and labelling approximate hierarchical self-similarity . International Conference on Music Information Retrieval . Vienna, Austria. pp. 41 – 46 .
   Google Scholar
• Serra , X. and Gomez , E. A cover song identification system based on sequences of tonal descriptors . Proceedings of the 8th International Symposium on Music Information Retrieval (ISMIR 2007) . Vienna, Austria.
   Google Scholar
• Tzanetakis , G. and Essl , G. 2001 . Automatic musical genre classification of audio signals . IEEE Transactions on Speech and Audio Processing , 10 ( 5 ) : 293 – 302 .
   Google Scholar
• van Rijsbergen , C. V. 1979 . Information Retrieval , New York : Butterworth .
   Google Scholar
• Wang , A. An industrial strength audio search algorithm . Fourth International Symposium on Music Information Retrieval . Baltimore, MD. pp. 7 – 13 .
   Google Scholar
• Wang , A. 2006 . The Shazam music recognition service . Communications of the ACM , 49 ( 8 ) : 44 – 48 .
   Google Scholar