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How do people compose music? Can computers, with statistics, create a mazurka that cannot be distinguished from a Chopin original? Tom Collins, Robin Laney, Alistair Willis and Paul H. Garthwaite make music with Markov models. [ABSTRACT FROM AUTHOR]

This study relates various quantifiable characteristics of a musical pattern to subjective assessments of a pattern's salience. Via score analysis and listening, twelve music undergraduates examined excerpts taken from Chopin's mazurkas. They were instructed to rate already discovered patterns, giving high ratings to patterns that they thought were noticeable and/or important. Each undergraduate rated thirty specified patterns and ninety patterns were examined in total. Twenty-nine quantifiable attributes (some novel but most proposed previously) were determined for each pattern, such as the number of notes a pattern contained. A model useful for relating participants' ratings to the attributes was determined using variable selection and cross-validation. Individual participants were much poorer than the model at predicting the consensus ratings of other participants. While the favored model contains only three variables, many variables were identified as having some predictive value if considered in isolation. Implications for music psychology, analysis, and information retrieval are discussed.

We describe the Open University team's submission to the 2011 i2b2/VA/Cincinnati Medical Natural Language Processing Challenge, Track 2 Shared Task for sentiment analysis in suicide notes. This Shared Task focused on the development of automatic systems that identify, at the sentence level, affective text of 15 specific emotions from suicide notes. We propose a hybrid model that incorporates a number of natural language processing techniques, including lexicon-based keyword spotting, CRF-based emotion cue identification, and machine learning-based emotion classification. The results generated by different techniques are integrated using different vote-based merging strategies. The automated system performed well against the manually-annotated gold standard, and achieved encouraging results with a micro-averaged F-measure score of 61.39% in textual emotion recognition, which was ranked 1st place out of 24 participant teams in this challenge. The results demonstrate that effective emotion recognition by an automated system is possible when a large annotated corpus is available.