, http://www.ircam.fr A huge amount of audio data is accessible to everyone by on-line or off-line information services and it is necessary to develop techniques to automatically describe and deal with this data in a meaningful way. In the particular context of music content processing it is important to take into account the melodic aspects of the sound. The goal of this article is to review the different techniques proposed for melodic description and extraction. Some ideas around the concept of melody are first presented. Then, an overview of the different ways of describing melody is done. As a third step, an analysis of the methods proposed for melody extraction is made, including pitch detection algorithms. Finally, techniques for melodic pattern induction and matching are also studied, and some useful melodic transformations are reviewed.
This paper is concerned with the handling of rhythm in music content processing applications. Keeping this framework in mind, we briefly report on terminology issues and the interdisciplinary nature of rhythm investigations, we then review approaches to computational modeling of rhythm and rhythm representation schemes. We comment the bottomup and top-down oriented approaches to computational modeling and the parallel that some authors make with physiological and cognitive views on rhythm perception. We argue that investigations should be listener-oriented, signal-oriented and application-oriented. Accepted for publication on May 9th 2002 − Retrieving any desired selection of items − Transforming items − Introducing new items Audio Event lists Symbols Abstract representations O p e r a te o n Processings: -Exploration -Understanding -Database browsing -Comparisons -Retrieval -Transformations -Populating the databaseFigure 1: Overview of music content processing. From musical data to meaningful representations.Further in the article, we will detail the issues of designing relevant representation schemes for musical data (in Section 3) and of identifying elements of these representations in musical data (in Section 2). Prior to these investigations, some additional terminology must be clarified.
In the context of musical analysis, we propose an algorithm that automatically induces patterns from polyphonies. We define patterns as "perceptible repetitions in a musical piece". The algorithm that measures the repetitions relies on some general perceptive notions: it is non-linear, non-symetric and non-transitive. The model can analyse any music of any genre that contains a beat. The analysis is performed into three stages. First, we quantize a MIDI sequence and we segment the music in "beat segments". Then, we compute a similarity matrix from the segmented sequence. The measure of similarity relies on features such as rhythm, contour and pitch intervals. Last, a bottom-up approach is proposed for extracting patterns from the similarity matrix. The algorithm was tested on several pieces of music, and some examples will be presented in this paper.
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