Computer music modeling and retrieval: International by Uffe K. Wiil

By Uffe K. Wiil

This booklet constitutes the completely refereed post-proceedings of the foreign computing device tune Modeling and Retrieval Symposium, CMMR 2003, held in Montpellier, France, in may possibly 2003.

The 20 revised complete papers have been rigorously chosen in the course of rounds of reviewing and development. end result of the interdisciplinary nature of the realm, the papers handle a large number of themes together with details retrieval, programming, human-computer interplay, electronic libraries, hypermedia, synthetic intelligence, acoustics, sign processing, etc.  The ebook comes with a CD-ROM offering supplementary fabric for the papers included.

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Additional info for Computer music modeling and retrieval: International Symposium, CMMR 2003, Montpellier, France, May 26-27, 2003: revised papers

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Causal relationships among chunks of music information and related transformations within the music score are described like they appear in the frame of compositional and analysis processes. Notational layer groups the meta-data and relationships about Score Category of the archive. Information contained in this layer is tied to the spatial part of Spine structure. Towards a General Architecture for Musical Archive Information Systems 27 SMI General Structural General Logical Organized Symbols Layers Structural Logic Music Music Logic Spine Notational Notational Performance Audio Performance Audio TIFF MP3 NIFF a) b) MIDI WAV MPEG4 Source Material Fig.

1 Communication between Modules The communication between modules is based on XML approach. We can distinguish among two types of XML messages: Commands and Data. Commands are represented with dotted arrow lines. While straight lines represent the passage of data between modules. We briefly describe the interaction of the modules. Towards a General Architecture for Musical Archive Information Systems 25 When a new source material about a piece of music is introduced into the system it is firstly placed into the Content Data Archive (CDA).

For each new peak (left), a number of previous intervals are scaled and added to the vector (right). The maximum of the beat probability vector gives the current beat interval. 2 Update with Multiple Intervals To avoid a situation where spurious peaks create a maximum in the probability vector with an interval that does not match the current beat, the vector is Real-Time Beat EstimationUsing Feature Extraction 21 updated in a novel way. By weighting each new note and taking multiple previous note onsets into account, the probability vector H(t) is updated with N previous weighted intervals that lie within the allowed beat interval, H(t) = H(t) + ΣN i=1 wk wk−i G(tk − tk−i , t), t = 0 .

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