Representations of musical signals
Representations of musical signals
C4.5: programs for machine learning
C4.5: programs for machine learning
From contingency tables to various forms of knowledge in databases
Advances in knowledge discovery and data mining
Principles of multimedia database systems
Principles of multimedia database systems
Rough Sets as A Tool for Audio Signal Classification
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
Musical instrument recognition using cepstral coefficients and temporal features
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
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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.