User-dependent Taxonomy Of Musical Features As A Conceptual
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Proceedings of the Stockholm Music Acoustics Conference, August 6-9, 2003 (SMAC 03), Stockholm, SwedenSMAC-1USER-DEPENDENT TAXONOMY OF MUSICAL FEATURES AS A CONCEPTUALFRAMEWORK FOR MUSICAL AUDIO-MINING TECHNOLOGY
Micheline Lesaffre1, Marc Leman1, Koen Tanghe1, Bernard De Baets3, Hans De Meyer4 and Jean-Pierre Martens2
1 Department of Musicology (IPEM), Ghent University, Blandijnberg 2, 9000-GHENT, Belgium,2 Department of Electronics and Information Systems (ELIS), Ghent University3 Department of Applied mathematics, Biometrics and Process Control, Ghent University4 Department of Applied Mathematics and Computer Science, Ghent UniversityMicheline.Lesaffre@rug.ac.be
ABSTRACTMusical audio-mining technology allows users to search andretrieve music by means of content-based text and audio queries.Though these systems are promising, there is a need for a betterunderstanding of the role of user preferences and user profiles.The development of taxonomies for different aspects of amusical audio-mining system aims at bridging the gap betweensystem development and user interactive interfacing. In the firstpart of this paper, the need for user-dependent taxonomydevelopment is addressed and an experiment in spontaneous userbehavior is described, based on 72 users and 1148 vocal queries.Statistical analysis provides insight into the characteristics ofvocal querying and possible useful concepts. In the second partof the paper, it is described how categories and concepts fromthe statistical analysis have been used for the refinement oftaxonomies that address user interactive interfacing and featureextraction.
1. INTRODUCTIONSearch and retrieval of information is a core activity of theinformation society and music is a product of interest to manymembers of this society. Hence the need for advanced musicinformation retrieval (MIR) systems, which allow users tospecify musical content interactively [1]. For this aim, reliableautomatic annotation and processing of musical content isneeded (see the proceedings of ISMIR 2000-2).
The extraction and processing of musical content frommusical audio is called musical audio mining. Figure 1 showsthe general architecture of an audio-based music retrievalsystem. It consists of a target audio-database (left), a queryinterface (right), and a similarity-matching engine with optionalparts that account for users profiling. The task is to retrieve thewanted musical audio files using information provided by thequery.
Figure 1: General architecture of an audio-based MIR system.
Left: the target structures with audio database and abstractrepresentation. Right: the query structures, with query input andabstract representations. In the middle, user with particularpreferences and profile. Bottom: query response (e.g. in the formof a list of titles)
A main problem, however, is how users want to interactwith those systems. Although there is general agreement that theuser-centered approach is a key area of music informationretrieval, few studies have been undertaken that concentrate onuser behavior [2]. Thus far, a variety of systems have beendeveloped that can automatically extract information from anaudio signal [3, 4] but less attention has been paid to the user-friendly characteristics of this interaction.
Progress in developing user-friendly musical audio-miningdepends on a number of factors, among which: (i) carefulanalysis of spontaneous user query behavior, (ii) thedevelopment of taxonomies, that is, sets of meaningful conceptsand relationships between concepts dealing with musicalcontent, (iii) the implementation of a system that uses thisProceedings of the Stockholm Music Acoustics Conference, August 6-9, 2003 (SMAC 03), Stockholm, SwedenSMAC-2network of concepts and interrelations for automatic annotationand processing of musical content, (iv) the design of a user-friendly query interface that deals with the spontaneous ways inwhich users tend to interact with music.
This paper highlights the approach of the MAMI-project inseveral of these domains. In the first part of this paper, the needfor user-dependent taxonomy development is addressed and anexperiment in spontaneous user behavior is described, based ona large-scale experiment with 72 users and 1148 vocal queries.Statistical analysis provides insight into the characteristics ofvocal querying and possible useful concepts. In the second partof the paper, it is described how categories and concepts fromthe statistical analysis have been used for the refinement oftaxonomies that address user interactive interfacing and featureextraction.