Titlebook: Exploring Music Contents; 7th International Sy S?lvi Ystad,Mitsuko Aramaki,Kristoffer Jensen Conference proceedings 2011 Springer-Verlag Gm

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Souslinmengen (allgemeine Eigenschaften),ive matrix factorization (NMF) and note classification, assuming that the instruments used to play the sound signals are known a priori. The spectrogram of the mixture signal is first decomposed into building components (musical notes) using an NMF algorithm. The Mel frequency cepstrum coefficients

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https://doi.org/10.1007/978-3-662-28439-1rforming blind source separation (BSS) of multichannel audio data. In this paper we investigate the statistical source models implied by this approach. We show that it implicitly assumes a nonpoint-source model contrasting with usual BSS assumptions and we clarify the links between the measure of fi

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,Pr?vention — Treibjagd auf die Krankheit, the BBC. In the first method, the machine estimated classifications obtained with an automatic timbre recognition (ATR) model are post-processed using median filtering. The classification system (LSF/K-means) was trained using two different taxonomic levels, a high-level one (speech, music), and a

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Ausgangssituation und Grundbegriffe,hat cannot be recognized are not often studied except by electroacoustic music composers. Besides, considerations on recognition of sources might help to address the problem of stimulus selection and categorization of sounds in the context of perception research. This paper introduces what we call a

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