1. MUSIC INFORMATION RETRIEVAL SYSTEMS Author: Amanda Cohen

2. Music Information Retrieval Systems  Based on content from the following webpage: http://mirsystems.info/index.php?id=mirsystems http://mirsystems.info/index.php?id=mirsystems  Other good sources on MIR and MIR systems  http://www.music-ir.org - Virtual home of music information retrieval research http://www.music-ir.org  http://www.ismir.net - The International Symposium on Music Information Retrieval http://www.ismir.net

3. Audentify!  Developers: F. Kurth, A. Ribbrock, M. Clausen  Relevant Publications:  Kurth, F., Ribbrock, A., Clausen, M. Identification of Highly Distorted Audio Material for Querying Large Scale Data Bases. 112th Convention of the Audio Engineering Society, May 2002, Munich, Convention Paper  Kurth, F., Ribbrock, A., Clausen, M. Efficient Fault Tolerant Search Techniques for Full-Text Audio Retrieval. 112th Convention of the Audio Engineering Society, May 2002, Munich, Convention Paper  Ribbrock, A. Kurth, F. A Full-Text Retrieval Approach to Content-Based Audio Identification. International Workshop on Multimedia Signal Processing. St. Thomas, US Virgin Islands, December 9-11, 2002  Kurth, F. A Ranking Technique for fast Audio Identification. International Workshop on Multimedia Signal Processing. St. Thomas, US Virgin Islands, December 9-11, 2002  Clausen, M., Kurth, F. A Unified Approach to Content-Based and Fault Tolerant Music Recognition, IEEE Transactions on Multimedia. Accepted for publication

4. Audentify  System Description  Takes signal queries (1-5 seconds, 96-128 kbps)  Searches by audio fingerprint  Returns a file ID that corresponds with a song in the database  Currently a part of the SyncPlayer system

5. SyncPlayer  Developers: F. Furth, M. Muller, D. Damm, C. Fremerey, A. Ribbrock, M. Clausen  Relevant Publications:  Kurth, F., Müller, M., Damm, D., Fremerey, Ch. Ribbrock, A., Clausen, M. SyncPlayer - An Advanced System for Multimodal Music Access, Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), London, GB  Kurth, F., Müller, M., Ribbrock, A., Röder, T., Damm, D., Fremerey, Ch. A Prototypical Service for Real-Time Access to Local Context-Based Music Information. Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), Barcelona, Spain. http://www- mmdb.iai.uni-bonn.de/download/publications/kurth-service-ismir04.pdfhttp://www- mmdb.iai.uni-bonn.de/download/publications/kurth-service-ismir04.pdf  Fremerey, Ch., SyncPlayer - a Framework for Content-Based Music Navigation, Diplomarbeit at the Multimedia Signal Processing Group Prof. Dr. Michael Clausen, University of Bonn, 2006, Bonn, Germany  URL: http://audentify.iai.uni-bonn.de/synchome/index.php?pid=01

6. SyncPlayer  System Description  Query type(s): audio files (mp3, wav, MIDI), lyrics, MusicXML, Score scans (primary data)  Generates “derived data” from query extracts features generates annotations compiles synchronization data  Submitted to SyncPlayer Server, which can perform three services (at present) audio identification (through audentify) provide annotations for a given song retrieval in lyrics annotation  SyncPlayer Client: audio-visual user interface, allow user to playback, navigate, and search in the primary data

7. ChoirFish  Developers: A. Van Den Berg, S. Groot  Relevant Publications:  Groot, S., Van Den Berg, A., The Singing Choirfish: An application for Tune Recognition, Proceedings of the 2003 Speech Recognition Seminar, LIACS 2003  URL: http://www.ookii.org/university/speech/default.as px

8. ChoirFish  System Description:  Query by humming  Contour features used for matching  Used Parson’s Code to determine contour Code is based on the direction of note transitions 3 characters for each possible direction: R: The note is the same frequency as the previous note D: The note is lower in frequency than the previous note U: The note is higher in frequency than the previous note Generated by changing the audio to the frequency domain via Fast Fourier Transform and using the highest frequency peak to determine pitch and pitch change

9. CubyHum  Developers: S. Pauws  Relevant Publications:  Pauws, S., CubyHum: A Fully Operational Query by Humming System, ISMIR 2002 Conference Proceedings (2002): 187--196, doi:10.1.1.108.8515  PDF of paper: http://ismir2002.ismir.net/proceedings/02-FP06-2.pdf http://ismir2002.ismir.net/proceedings/02-FP06-2.pdf

10. CubyHum  System Description:  Query by Humming: user queries system by humming the desired song  Pitch is estimated by computing the sum of harmonically compressed spectra (sub-harmonic summation, or SHS).  Musical events (note onsets, gliding tones, inter-onset-intervals) are detected  Query is transformed via quantization into musical score, which is used to create a MIDI melody for auditory feedback  Approximate pattern matching used to find matching song  Distance between melodies defined based on interval sizes and duration ratios to compensate for imperfect query (people don’t always hum the correct melody in the correct key)

11. Fanimae  Developers: Iman S.H. Suyoto, Alexandra L. Uitdenbogerd and Justin Zobel  Relevant Publications  Suyoto, I.S.H., Uitdenbogerd, A.L., Simple efficient n- gram indexing for effective melody retrieval, Proceedings of the Annual Music Information Retrieval Evaluation eXchange, 2005  URL: http://mirt.cs.rmit.edu.au/fanimae/http://mirt.cs.rmit.edu.au/fanimae/

12. Fanimae  System Description:  Desktop Music Information Retrieval System  Search by symbolic melodic similarity  Query: a melody sequence that contains both pitch and duration information  Melody sequence is standardized Intervals are encoded as a number of semitones, with large intervals being reduced  Coordinate matching used to detect melodic similarity Query is split into n-grams of length 5, as are any possible answers count the common distinct terms between query and possible answer return results ranked by similarity

13. Foafing the Music  Developers: Music Technology Group of the Universitat Pompeu Fabra  Relevant Publications:  Celma, O. Ramírez, M. Herrera, P., Foafing the music: A music recommendation system based on RSS feeds and user preferences Proceedings of 6th International Conference on Music Information Retrieval; London, UK, 2005, http://ismir2005.ismir.net/proceedings/3119.pdf  URL: http://foafing-the-music.iua.upf.eduhttp://foafing-the-music.iua.upf.edu

14. Foafing the Music  System Description:  Returns personalized music recommendations based on a user’s profile (listening habits, location)  Bases recommendation information on info gathered across the web Similarity between artists determined by their relationships between one another (ex: influences, followers)  Creates RSS feed for news related to favorite artists  Computes musical similarity between specific songs

15. Meledex/Greenstone  Developers: McNab, Smith, Bainbridge, Witten  Relevant Publications:  McNab, Smith, Bainbridge, Witten, The New Zealand digital library MELody inDEX, D-Lib Magazine, May 1997  URL: http://www.nzdl.org/fast-cgi- bin/music/musiclibraryhttp://www.nzdl.org/fast-cgi- bin/music/musiclibrary

16. Meldex/Greenstone  System Description:  Receives audio queries (hummed, sung, or played audio)  Filters audio to get fundamental frequency  Input sent to pitch tracker, which returns average pitch estimate for each 20ms  Note duration can optionally be taken into account, as well as user defined tuning  Results found using approximate string matching based on melodic contour

17. Musipedia/Melodyhound/Tuneserver  Developer: Rainer Typke  Relevant Publications:  Prechelt, L., Typke, R., An Interface for Melody Input. ACM Transactions on Computer-Human Interaction, June 2001  URL: http://www.musipedia.orghttp://www.musipedia.org

18. Musipedia/Melodyhound/Tuneserver  System Description  Query by humming system  Record sound, system converts into sound wave  Converts query sound wave into Parson’s Code  Match by melodic contour Determine distance between query and possible results via editing distance (calculate the number of modifications necessary to turn one string into the other) Return results with smallest distance

19. MIDIZ  Developers: Maria Cláudia Reis Cavalcanti, Marcelo Trannin Machado, Alessandro de Almeida Castro Cerqueira, Nelson Sampaio Araujo Júnior and Geraldo Xexéo  Relevant Publications:  Cavalcanti, Maria Cláudia Reis et al. MIDIZ: content based indexing and retrieving MIDI files. J. Braz. Comp. Soc. [online]. 1999, vol. 6, no. 22008-11-02]. http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0 104-65001999000300002&lng=&nrm=iso ISSN 0104- 6500. doi: 10.1590/S0104-65001999000300002

20. MIDIZ  System description  Database that stores, indexes, searches for and recovers MIDI files based on the description of a short musical passage  Allows for non-exact queries  Musical sequence is based on intervals between notes  Uses wavelet transform and a sliding window in the melody Window defines a note sequence of a given size (2^k) and moves through the song note by note Each sequence in the window is converted into a vector storing the interval distances  First note in a sequence is assigned the value 1  Values of the following notes are determined by their chromatic distance in relation to the first note  Those values are added together in pairs, and the result is converted into coordinates in the final vector  Songs in database are stored in a BD Tree, determined by Discriminator Zone Expression  Completed vector of query is submitted to tree, similar results are returned

21. Mu-seek  Developers: Darfforst LLP  URL: http://www.mu-seek.com/http://www.mu-seek.com/  System Description:  Search by title, lyrics, tune fragment, or MIDI  Uses pitch, contour, and rhythm to find matches

22. MusicSurfer  Developers: Music Technology Group of the Universitat Pompeu Fabra  Relevant Publications:  Cano et al. An Industrial-Strength Content-based Music Recommendation System, Proceedings of 28th Annual International ACM SIGIR Conference; Salvador, Brazil 2005. http://mtg.upf.edu/files/publications/3ac0d3- SIGIR05-pcano.pdf  URL: http://musicsurfer.iua.upf.edu/http://musicsurfer.iua.upf.edu/

23. MusicSurfer  System Description:  Automatically extracts features from songs in database based on rhythm, instrumentation, and harmony Uses spectral analysis to determine timbre  Uses those features to search for similar songs

24. NameMyTune  Developers: Strongtooth, Inc  URL: http://www.namemytune.com/http://www.namemytune.com/  System Description:  User hums query into microphone  Results are found by other users determining what the song is

25. Orpheus  Developers: Rainer Typke  Relevant Publications:  Typke, Giannopoulos, Veltkamp, Wiering, van Oostrum, Using Transportation Distances for Measuring Melodic Similarity, ISMIR 2003  URL: http://teuge.labs.cs.uu.nl/Ruu/?id=5http://teuge.labs.cs.uu.nl/Ruu/?id=5

26. Orpheus  System Description:  Query can be example from database, hummed or whistled melody, or a MIDI file  All queries are converted into internal database format before submission  Similarity between query and results based on Earth Mover’s Distance Two distributions are represented by signatures Distance represents the amount of “work” required to change one signature to the other Work = user defined distance between two signatures

27. Probabilistic “Name That Song”  Developers: Eric Brochu and Nando de Freitas  Publications:  Brochu, E., Freitas, N.D., "Name That Song!": A Probabilistic Approach to Querying on Music and Text. NIPS. Neural Information Processing Systems: Natural and Synthetic 2002 (2003)

28. Probabilistic “Name That Song”  System Description:  Query is composed of note transitions (Q m ) and words (Q t ). A match is found when a corresponding song has all elements of Q m and Q t with a frequency of 1 or greater.  Database songs are clustered. Query is performed on each song in each cluster until a match is found

29. Query by Humming (Ghias et al.)  Developers: Asif Ghias, Jonathan Logan, David Chamberlin, Brian C. Smith  Relevant Publications:  Ghias, A., Logan, J., Chamberlin, D., Smith, B.C., Query by Humming - Musical Information Retrieval in an Audio Database, ACM Multimedia (1995)  URL: http://www.cs.cornell.edu/Info/Faculty/bsmith/quer y-by-humming.html http://www.cs.cornell.edu/Info/Faculty/bsmith/quer y-by-humming.html

30. Query by Humming (Ghias et al.)  System Description:  Hummed queries are recorded in Matlab  Pitch tracking is performed Converted into a string of intervals similar to Parson’s Code (U/D/S used as characters instead of R/D/U)  Baesa-Yates/Perleberg pattern matching algorithm used to find pattern matches Find all instances of the query string in the result string with at most k mismatches Results returned in order of how they best fit the query

31. Search by Humming  Developers: Steven Blackburn  Relevant Publications:  Blackburn, S. G., Content Based Retrieval and Navigation of Music Using Melodic Pitch Contours. PhD Thesis, 2000  Blackburn, S. G., Content Based Retrieval and Navigation of Music. Masters, 1999  DeRoure, D., El-Beltagy, S., Blackburn, S. and Hall, W., A Multiagent System for Content Based Navigation of Music. ACM Multimedia 1999 Proceedings Part 2, pages 63-6.  Blackburn, S. G. and DeRoure, D. C., A tool for content based navigation of music. Proceedings of ACM Multimedia 1998, pages 361—368  DeRoure, D. C. and Blackburn, S. G., Amphion: Open Hypermedia Applied to Temporal Media, Wiil, U. K., Eds. Proceedings of the 4th Open Hypermedia Workshop, 1998, pages 27--32.  DeRoure, D. C., Blackburn, S. G., Oades, L. R., Read, J. N. and Ridgway, N., Applying Open Hypermedia to Audio, Proceedings of ACM Hypertext 1998, pages 285--286.  URL: http://www.beeka.org/research.htmlhttp://www.beeka.org/research.html

32. Search by Humming  System Description:  Takes query by humming, example, or MIDI  Queries and database contents represented by gross melodic pitch contour Within database, each track is stored as a set of overlapping sub-contours of a constant length  Distance between songs is determined by the minimum cost of transforming one contour into another (similar to EMD)  Query is expanded into a set of all possible contours of the same length as the database’s sub-contours  A score is calculated for each file based on the number of times a contour in the expanded query set occurs in the file. Results are sorted in order of score

33. SOMeJB (The SOM enhanced JukeBox)  Developers: Andreas Rauber, Markus Frühwirth, E. Pampalk, D. Merkl  Relevant Publications:  A. Rauber, E. Pampalk, D. Merkl, The SOM-enhanced JukeBox: Organization and Visualization of Music Collections based on Perceptual Models, Journal of New Music Research (JNMR), Swets and Zeitlinger, 2003  E. Pampalk, A. Rauber, D. Merkl, Content-based Organization and Visualization of Music Archives In: Proceedings of ACM Multimedia 2002, pp. 570-579, December 1-6, 2002, Juan-les-Pins, France  A. Rauber, E. Pampalk, D. Merkl, Using Psycho-Acoustic Models and Self-Organizing Maps to Create a Hierarchical Structuring of Music by Musical Styles, Proceedings of the 3rd International Symposium on Music Information Retrieval (ISMIR 2002), pp. 71-80, October 13-17, 2002, Paris, France.  A. Rauber, E. Pampalk, D. Merkl, Content-based Music Indexing and Organization, Proceedings of the 25. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 02), pp. 409-410, August 11-15, 2002, in Tampere, Finland  A. Rauber, and M. Frühwirth, Automatically Analyzing and Organizing Music Archives, Proceedings of the 5. European Conference on Research and Advanced Technology for Digital Libraries (ECDL 2001), Sept. 4-8 2001, Darmstadt  URL: http://www.ifs.tuwien.ac.at/~andi/somejbhttp://www.ifs.tuwien.ac.at/~andi/somejb

34. SOMeJB (The SOM enhanced JukeBox)  System Description:  Interface is a static, web-based map where similar pieces of music are clustered together  Music organized by a novel set of features based on rhythm patterns in a set of frequency bands and psycho-acoustically motivated transformations Extracts features that apply to loudness sensation (intensity), and rhythm  Self-organizing map algorithm is applied to organize the pieces on a map (trained neural network)

35. SoundCompass  Developers: Naoko Kosugi, Yuichi Nishihara, Tetsuo Sakata, Masashi Yamamuro and Kazuhiko Kushima, NTT Laboratories  System Description:  User sets a metronome and hums melody in time with clicks  Database songs have three feature vectors Tone Transition Feature Vector: contains the dominant pitch for each 16-beat window Partial Tone Transition Feature Vector: Covers a time window different from the Tone Transition Feature Vector  Tone Distribution Feature Vector: histogram containing note distribution  Query is matched against each of the vectors, results are combined by determining the minimum

36. Tararira  Developers: Ernesto López, Martin Rocamora, Gonzalo Sosa  Relevant Publications:  E. Lopez y M. Rocamora. Tararira: Sistema de búsqueda de música por melodía cantada. X Brazilian Symposium on Computer Music. October, 2005.  URL: http://iie.fing.edu.uy/investigacion/grupos/gmm/p royectos/tararira/ http://iie.fing.edu.uy/investigacion/grupos/gmm/p royectos/tararira/

37. Tararira  System Description:  User submits a hummed query  Pitch tracking applied to query  Audio segmentation determines note boundaries  Melodic analysis adjusts pitches to tempered scale  Results found by coding query note sequence, find occurrences using flexible similarity rules (string matching), and refining the selection using pitch time series

38. TreeQ  Developer: Jonathan Foote  Publications:  Foote, J.T., Content-Based Retrieval of Music and Audio, C.-C. J. Kuo et al., editor, Multimedia Storage and Archiving Systems II, Proc. of SPIE, Vol. 3229, pp. 138- 147, 1997  URL: http://sourceforge.net/projects/treeq/http://sourceforge.net/projects/treeq/

39. TreeQ  System Description:  Primarily query by example, can also search by classification  Tree based supervised vector quantizer is built from labeled training data Database audio is parameterized via conversion into MFCC and energy vectors Each resulting vector is quantized into the tree Vector space divided into “bins”, any MFCC vector will fall into one bin A histogram based on the distribution of MFCC vectors into each bin is created for query and database audio  Songs matched based on histograms of feature counts at tree leaves Distance is determined using Euclidian distance between corresponding templates of each audio clip Results sorted by magnitude and returned as a ranked list

40. VisiTunes  Developers: Scott McCaulay  Further Information: www.slis.indiana.edu/research/phd_forum/2006/mcca ulay.doc  URL: http://www.naivesoft.com/ http://www.naivesoft.com/  System Description:  Uses audio content of songs to calculate similarity between music and creates playlists based on the results Converts sample values of each frame to frequency data Extracts sum total of sound energy by frequency band Uses results to simplify audio data into 256 integer values for fast comparison