Analysis of Feature Dependencies in Sound Description

Journal of Intelligent Information Systems (Impact Factor: 0.89). 05/2003; 20(3):285-302. DOI: 10.1023/A:1022864925044
Source: DBLP


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.

Download full-text


Available from: Alicja Wieczorkowska, Dec 22, 2014
  • [Show abstract] [Hide abstract]
    ABSTRACT: Signal Processing Methods for Music Transcription is the first book dedicated to uniting research related to signal processing algorithms and models for various aspects of music transcription such as pitch analysis, rhythm analysis, percussion transcription, source separation, instrument recognition, and music structure analysis. Following a clearly structured pattern, each chapter provides a comprehensive review of the existing methods for a certain subtopic while covering the most important state-of-the-art methods in detail. The concrete algorithms and formulas are clearly defined and can be easily implemented and tested. A number of approaches are covered, including, for example, statistical methods, perceptually-motivated methods, and unsupervised learning methods. The text is enhanced by a common reference and index. This book aims to serve as an ideal starting point for newcomers and an excellent reference source for people already working in the field. Researchers and graduate students in signal processing, computer science, acoustics and music will primarily benefit from this text. It could be used as a textbook for advanced courses in music signal processing. Since it only requires a basic knowledge of signal processing, it is accessible to undergraduate students. © 2006 Springer Science+Business Media LLC. All rights reserved.
    No preview · Book · Jan 2006
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Academics and librarians have yet to reach a consensus on the indexing of print resources about music, nor have they developed satisfactory means of indexing sheet music. With the increasing presence of audio music on the Internet, the need to properly index MP3s and other audio files has reached a new level of urgency, and with it the need to label these items satisfactorily to enable retrieval. While the importance of these fields has been constant since the beginning of indexing and cataloguing, increased availability of sources means that there is more music available to users than ever before, but little in the way of sorting through it. Luckily studies are being undertaken with the aim of solving these problems. This article seeks to explore and explain some of these developments.
    Preview · Article · Nov 2010