Local Models in Register Classification by Timbre

Weihs, Claus; Szepannek, Gero; Ligges, Uwe; Luebke, Karsten; Raabe, Nils

doi:10.1007/3-540-34416-0_34

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…Characteristics of the register of instruments or voices (such as basso, tenor, alto and soprano) have been analyzed by Weihs et al (2006b) using local probabilistic models for various groups of instruments based on masses and widths of the peaks of so-called pitch-independent periodograms (Weihs and Ligges 2005). In Berenzweig et al (2003) a supervised approach is taken to model certain aspects of musical sound, e.g., genre or the singer's gender.…”

Section: Semantic Featuresmentioning

confidence: 99%

Classification in music research

Weihs

Ligges

Mörchen

et al. 2007

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Since a few years, classification in music research is a very broad and quickly growing field. Most important for adequate classification is the knowledge of adequate observable or deduced features on the basis of which meaningful groups or classes can be distinguished. Unsupervised classification additionally needs an adequate similarity or distance measure grouping is to be based upon. Evaluation of supervised learning is typically based on the error rates of the classification rules. In this paper we first discuss typical problems and possible influential features derived from signal analysis, mental mechanisms or concepts, and compositional structure. Then, we present typical solutions of such tasks related to music research, namely for organization of music collections, transcription of music signals, cognitive psychology of music, and compositional structure analysis.

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Section: Semantic Featuresmentioning

confidence: 99%