Analysis of feature dependencies in sound description

Journal of Intelligent Information Systems (Impact Factor: 0.83). 01/2003; 20:285-302. DOI: 10.1023/A:1022864925044
Source: DBLP

ABSTRACT Multimedia data, including sound databases, require signal processing and parameterization to enable automatic searching for a specific content. Indexing of musical audio material with high-level timbre information requires extraction of low-level sound parameters first. In this paper, we analyze regularities in musical sound description, for the data representing musical instrument sounds by means of spectral and time-domain features. We examined digital audio recordings of singular sounds for 11 instruments of definite pitch. Woodwinds, brass, and strings used in contemporary orchestras were investigated, for various fundamental frequencies of sound and articulation techniques. General-purpose data mining system Forty-Niner was applied to investigate dependencies between the sound attributes, and the results of the experiments are presented and discussed. We also indicate a broad range of possible industry applications, which may influence directions of further research in this domain. We summarize our paper with conclusions on representation of musical instrument sound, and the emerging issue of exploration of audio databases.

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    ABSTRACT: A study on the automatic classification of musical instrument sounds is presented. A database of musical instrument sounds parameters was built for this purpose, which consists of musical instrument recordings and their parametric representation. The parameterization process was conceived and performed in order to find significant musical instrument sound features and to remove redundancy from the musical signal. Classification experiments of musical instrument sounds were performed with neural networks allowing a discussion of the feature extraction process efficiency and of its limitations. Conclusions and remarks concerning further development of this study and its relation to the current MPEG-7 standardization process are included.
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    ABSTRACT: Large databases can be a source of useful knowledge. Yet this knowledge is implicit in the data. It must be mined and expressed in a concise, useful form of statistical patterns, equations, rules, conceptual hierarchies, and the like. Automation of knowledge discovery is important because databases are growing in size and number, and standard data analysis techniques are not designed for exploration of huge hypotheses spaces. We concentrate on discovery of regularities, defining a regularity by a pattern and the range in which that pattern holds. We argue that two types of patterns are particularly important: contingency tables and equations, and we present Forty-Niner (49er), a general-purpose database mining system which conducts large-scale search for those patterns in many subsets of data, conducting a more costly search for equations only when data indicate a functional relationship. 49er can refine the initial regularities to yield stronger and more general regularities and more useful concepts. 49er combines several searches, each contributing to a different aspect of a regularity. Correspondence between the components of search and the structure of regularities makes the system easy to understand, use, and expand. Finally, we discuss 49er's performance in four categories of tests: (1) open exploration of new databases; (2) reproduction of human findings (limited because databases which have been extensively explored are very rare); (3) hide- and -seek testing on artificially created data, to evaluate 49er on large scale against known results; (4) exploration of randomly generated databases.
    Journal of Intelligent Information Systems 02/1993; 2(1):39-81. · 0.83 Impact Factor

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