Computer Music Modeling and Retrieval: Second International by Elaine Chew, Xiaodan Wu (auth.), Uffe Kock Wiil (eds.)

By Elaine Chew, Xiaodan Wu (auth.), Uffe Kock Wiil (eds.)

This booklet constitutes the completely refereed post-proceedings of the foreign machine song Modeling and Retrieval Symposium, CMMR 2004, held in Esbjerg, Denmark in may possibly 2004.

The 26 revised complete papers awarded have been conscientiously chosen in the course of rounds of reviewing and development. a result of interdisciplinary nature of the world, the papers deal with a large number of issues. The papers are prepared in topical sections on pitch and melody detection; rhythm, pace, and beat; song new release and data; tune functionality, rendering, and interfaces; song rankings and synchronization; synthesis, timbre, and musical enjoying; track illustration and retrieval; and tune analysis.

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Extra resources for Computer Music Modeling and Retrieval: Second International Symposium, CMMR 2004, Esbjerg, Denmark, May 26-29, 2004. Revised Papers

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It is likely that the brain measures periodicities using a neural delay line, a case that is supported by the cross-correlator structures found in the brains of owls and cats. Furthermore, the detection of periodicities is also inspired by the well-known “timing theory” of auditory nerve firing. 28 Rui Pedro Paiva, Teresa Mendes, and Amílcar Cardoso In terms of computer implementation, here, the periodicities in the cochleagram are obtained by computing the short-time ACF of the neural firing responses in each cochlear channel for a particular time window.

In fact, Serra looks for regions of stable sinusoids in the signal’s spectrum, which leads to a trajectory for each harmonic component found. Therefore, a high number of trajectories have to be processed, which makes the algorithm much heavier, though the basic idea is the same. Another difference is that we first quantize frequencies to the closest MIDI note. We found that peak continuation based on MIDI note numbers allows for a more robust trajectory build up. One reason for this seems to come from the fact that the location of peaks oscillates somewhat due to interference from other sources in the sound mixture.

Since many of the obtained notes are irrelevant in a melody extraction context, short-duration, low-salience and harmonically-related notes are eliminated. Finally, the notes comprising the detected melody are extracted by selecting the most salient notes at each time. For the sake of visualization simplicity, we will illustrate the method with a simple example: a monophonic saxophone riff. Raw Musical Signal Melody Notes Melody Detection System MPD MPTC Trajectory Segmentation Note Elimination Melody Extraction Fig.

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