Dunya: A System To Browse Audio Music Collections Exploiting Cultural Context (original) (raw)
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IEEE Multimedia, 2000
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With increasing amounts of music being available in digital form, research in music information retrieval has turned into a dominant field to support organization of and easy access to large collections of music. Yet, most research is focussed traditionally on Western music, mostly in the form of mastered studio recordings. This leaves the question whether current music information retrieval approaches can also be applied to collections of non-Western and in particular ethnic music with completely different characteristics and requirements.In this work we analyze the performance of a range of automatic audio description algorithms on three music databases with distinct characteristics, specifically a Western music collection used previously in research benchmarks, a collection of Latin American music with roots in Latin American culture, but following Western tonality principles, as well as a collection of field recordings of ethnic African music. The study quantitatively shows the advantages and shortcomings of different feature representations extracted from music on the basis of classification tasks, and presents an approach to visualize, access and interact with ethnic music collections in a structured way.
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Our information technologies do not respond to the world's multicultural reality; in fact, we are imposing the paradigms of our market-driven western culture also on IT, thus facilitating the access of a small part of the world's information to a small part of the world's population. The current IT research efforts may even make it worse, and future IT will accentuate this information bias. Most IT research is being carried out with a western centered approach and as a result, most of our data models, cognition models, user models, interaction models, ontologies, etc., are culturally biased. This fact is quite evident in music information research, since, despite the world's richness in terms of musical culture, most research is centered on CDs and metadata of western commercial music. This is the motivation behind a large and ambitious project funded by the European Research Council entitled "CompMusic: Computational Models for the discovery of the world's music." In this paper we present the ideas supporting this project, the challenges that we want to work on, and the proposed approaches to tackle these challenges.
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The amount of digital music has grown unprecedentedly during the last years and requires the development of effective methods for search and retrieval. In particular, content-based preference elicitation for music recommendation is a challenging problem that is effectively addressed in this paper. We present a system which automatically generates recommendations and visualizes a user's musical preferences, given her/his accounts on popular online music services. Using these services, the system retrieves a set of tracks preferred by a user, and further computes a semantic description of musical preferences based on raw audio information. For the audio analysis we used the capabilities of the Canoris API. Thereafter, the system generates music recommendations, using a semantic music similarity measure, and a user's preference visualization, mapping semantic descriptors to visual elements.
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Search and retrieval of specific musical content such as emotive or sonic features has become an important aspect of Music Information Retrieval system development, but only little research is user-oriented. We summarize results of an elaborate user-study that explores who the users of music information retrieval systems are and what structural descriptions of music best characterize their understanding of music expression. Our study reveals that perceived qualities of music are affected by the context of the user. Subject dependencies are found for age, music expertise, musicianship, taste and familiarity with the music. Furthermore, interesting relationships are discovered between expressive and structural features. These findings are validated by means of a Semantic Music Recommender System prototype. The demonstration system recommends music from a database containing the quality ratings provided by the participants in a music annotation experiment. A test in the real world reve...
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Music Recommendation System, Journal of Telecommunications and Information Technology, 2014, nr 2
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