Music Information Retrieval. Juan Pablo Bello MPATE-GE 2623 Music Information Retrieval New York University

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Music Information Retrieval Juan Pablo Bello MPATE-GE 2623 Music Information Retrieval New York University 1

Juan Pablo Bello Office: Room 626, 6th floor, 35 W 4th Street (ext. 85736) Office Hours: Wednesdays 2-5pm email: jpbello@nyu.edu Personal webpage: https://files.nyu.edu/jb2843/public/home.html This course: http://www.nyu.edu/classes/bello/mir.html 2

Music Information Retrieval Aims at extending the understanding and usefulness of music data, through the research, development and application of computational approaches and tools. Grounded in the combined use of theories, concepts and techniques from music, computer science, signal processing and cognition. Music information: bibliographical, surveys, tags, scores, MIDI, audio, etc. This course focuses on the analysis of audio signals (a very rich source of music information) Content or audio-based MIR? Music Signal Processing? Machine Listening? 3

For example... BPM histogram Tempogram Detection Function Spectrogram Audio Signal Electronica Arousing Fast Folk David Gray Please Forgive me Singer-songwriter British 4

MIR Industry (a few examples) Audio Identification: Shazam, Gracenote Score Following: Rock Prodigy, SmartMusic, Rockband Automatic Music Transcription: Zenph Recommendation, Playlisting: Google Music, Last.fm, Pandora, Spotify Machine Listening: Echonest (BBC, MTV and many more) For examples of a vibrant community: Music Hack Day 5

MIR Research community Organized into the International Society for Music Information Retrieval and its conference (ISMIR) Last Conference: Utrecht, The Netherlands (http://ismir2010.ismir.net/) Next Conference: Miami, USA, October 24-28 (http://ismir2011.ismir.net/) Highly multidisciplinary: Library and Information Science, Computer Science, Music/Musicology, Electronic Engineering, Psychology, Law, etc. + Papers and sessions at ICASSP, NIPS, SMT, ICMC, SMC, DAFx, SIGIR 6

MIR Research community ISMIR home and mailing list: http://www.ismir.net/ Cumulative list of all ISMIR papers: http://www.ismir.net/all-papers.html MIR-related PhD theses: http://www.pampalk.at/mir-phds/ MIR Evaluation exchange (MIREX): http://www.music-ir.org/mirex/wiki/ MIREX_HOME Million Song Dataset: http://labrosa.ee.columbia.edu/millionsong/ 7

Calendar: Lectures Week 1-2 Time-frequency representations Week 3-4 Novelty: onset detection Week 5-6 Periodicity: pitch and beat tracking Week 7-8 Low-level features: timbre analysis Week 9-10 Harmony: alignment, chord and key recognition Week 11 Structure: form analysis, segmentation Week 12-13 Sound classification: autotagging, artist and instrument ID 8

Assessment Assignments: 50% (best 4 out of 5): announced in class/website, due a week after posting, penalties will apply to delays of up to 2 days. Projects: 40% (groups of 2) Proposal (11.05): 10% Final project + presentation (12.17): 30% Class Participation: 10% (readings + discussions, attendance, interest and enthusiasm) 9

Calendar: Important dates Fall 2012 9.3 - Labour day 9.14 - (Friday 5-7.30pm) MBus/MTech meet and greet 10.08 - ISMIR-12 - Recitation 10.15 - Columbus day 12.12 - (Wednesday on monday schedule) - Last lecture 12.17 - Final project submission and presentation 10

Tutoring/Resources TA: Eric Humphrey (ejh333@nyu.edu), TBD, Room 623 TA: Cheng-i Wang (ciw216@nyu.edu), TBD, 6th floor USE THE OFFICE HOURS (Wednesdays 2-5pm) All relevant information is (or will be published) on the class website - Please read it carefully and keep checking for updates. http://www.nyu.edu/classes/bello/mir.html 11

Recommended Reading Lerch, A. An Introduction to Audio Content Analysis. John Wiley & Sons (2012) Li, T., Ogihara, M. and Tzanetakis, G. Music Data Mining. CRC Press (2012) Klapuri, A. and Davy, M. (Eds.) Signal Processing Methods for Music Transcription. Springer (2006) Müller, M. Information Retrieval for Music and Motion. Springer (2007) Smith, J.O. Mathematics of the Discrete Fourier Transform (DFT). 2nd Edition, W3K Publishing (2007) Witten, I. and Frank, E. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann (2005) Further reading will be recommended as the course progresses. 12

To do INSTALL MATLAB ASAP! Matlab documentation, tutorials, examples: www.mathworks.com/access/ helpdesk/help/techdoc/matlab.html Signal Processing Toolbox documentation, tutorials, examples: www.mathworks.com/access/helpdesk/help/toolbox/signal/ Matlab file exchange: www.mathworks.com/matlabcentral/fileexchange/ loadcategory.do START LOOKING FOR PROJECT TOPIC: Visit MIR Community links, talk to current members of the MARL-MIR group (meets wednesdays 10am in 6th floor conference room), Attend the Friday seminars. 13