Changes between Version 3 and Version 4 of Publications

Timestamp:

Apr 11, 2008, 6:30:33 PM (16 years ago)

Author:

Paul Brossier

Comment:

added Yang paper, fix punctuation

Publications

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 == Co-authored papers ==
 
-A. Hazan, P. Brossier, P. Holonowicz, P. Herrera, and H. Purwins. [http://mtg.upf.edu/publications/100be8-ICMC07-Hazan.pdf Expectation Along The Beat: A Use Case For Music Expectation Models], in ''Proceedings of International Computer Music Conference 2007'', Copenhagen, Denmark, p. 228-236, 2007.
+A. Hazan, P. Brossier, P. Holonowicz, P. Herrera, and H. Purwins. [http://mtg.upf.edu/publications/100be8-ICMC07-Hazan.pdf Expectation Along The Beat: A Use Case For Music Expectation Models], in ''Proceedings of International Computer Music Conference 2007'' (ICMC 2007), Copenhagen, Denmark, p. 228-236, 2007.
     ''Abstract'': We present a system to produce expectations based on the observation of a rhythmic music signals at a constant tempo. The algorithms we use are causal, in order be fit closer to cognitive constraints and allow a future real-time implementation. In a first step, an acoustic front-end based on the aubio library extracts onsets and beats from the incoming signal. The extracted onsets are then encoded in a symbolic way using an unsupervised scheme: each hit is assigned a timbre cluster based on its timbre features, while its inter-onset interval regarding the previous hit is computed as a proportion of the extracted tempo period and assigned an inter-onset interval cluster. In a later step, the representation of each hit is sent to an expectation module, which learns the statistics of the symbolic sequence. Hence, at each musical hit, the system produces both what and when expectations regarding the next musical hit. For evaluating our system, we consider a weighted average F-measure, that takes into account the uncertainty associated with the unsupervised encoding of the musical sequence. We then present a preliminary experiment involving generated musical material and propose a roadmap in the context of this novel application field.
 
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 == Other Contributions ==
 
-W. You and R. B. Dannenberg [http://ismir2007.ismir.net/proceedings/ISMIR2007_p279_you.pdf Polyphonic Music Note Onset Detection Using Semi-Supervised Learning], in ''Proceedings of the 8th International Conference on Music Information Retrieval'' (ISMIR 2007), Vienna, Austria, September 23-27, 2007.
+W. You and R. B. Dannenberg. [http://ismir2007.ismir.net/proceedings/ISMIR2007_p279_you.pdf Polyphonic Music Note Onset Detection Using Semi-Supervised Learning], in ''Proceedings of the 8th International Conference on Music Information Retrieval'' (ISMIR 2007), Vienna, Austria, September 23-27, 2007.
     ''Abstract'': Automatic note onset detection is particularly difficult in orchestral music (and polyphonic music in general). Machine learning offers one promising approach, but it is lim- ited by the availability of labeled training data. Score-to-audio alignment, however, offers an economical way to locate onsets in recorded audio, and score data is freely available for many orchestral works in the form of standard MIDI files. Thus, large amounts of training data can be generated quickly, but it is limited by the accuracy of the alignment, which in turn is ultimately related to the problem of onset detection. Semi-supervised or bootstrapping techniques can be used to iteratively refine both onset detection functions and the data used to train the functions. We show that this approach can be used to improve and adapt a general purpose onset detection algorithm for use with orchestral music.
 
+A. C. Yang, E. Chew, and A. Volk. [http://ieeexplore.ieee.org/iel5/10471/33218/01565885.pdf A dynamic programming approach to adaptive tatum assignment for rhythm transcription], in ''Seventh IEEE International Symposium on Multimedia'', December 12-14, 2005.
+    ''Abstract'': We present a method for segmenting music with different grid levels in order to properly quantize note values in the transcription of music. This method can be used in automatic music transcription systems and music information retrieval systems to reduce a performance of a music piece to the printed or digital score. The system takes only the onset data of performed music from either MIDI or audio, and determine the best maximal grid level onto which to fit the note onsets. This maximal grid level, or tatum, is allowed to vary from section to section in a piece. We obtain the optimal segmentation of the piece using dynamic programming. We present results from an audio based performance of Milhaud's Botafogo, as well as several MIDI performances of the Rondo-Allegro from Beethoven's Pathetique. The results show a reduction of error compared to quantization based only on one global metric level, and promises to create rhythm transcriptions that are parsimonious and readable.