The present study addresses the problem of defining musical notes from pitch tracks, in the context of a system for melody detection in polyphonic musical signals. This is an important issue for melody transcription, as well as melody-based music information retrieval. Previous work in the area tackled mainly the extraction of melodic pitch lines, without explicit determination of musical notes. Therefore, in this paper we propose an approach for the creation of musical notes based on a two-stage segmentation of pitch tracks. In the first step, frequency-based segmentation is carried out through the detection of frequency variations in pitch tracks. In the second stage, salience-based segmentation is performed so as to split consecutive notes with equal value, by making use of salience minima and note onsets.

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In this paper we present an approach to emotion classification in audio music. The process is conducted with a dataset of 903 clips and mood labels, collected from Allmusic1 database, organized in five clusters similar to the dataset used in the MIREX2 Mood Classification Task. Three different audio frameworks – Marsyas, MIR Toolbox and Psysound, were used to extract several features. These audio features and annotations are used with supervised learning techniques to train and test various classifiers based on support vector machines. To access the importance of each feature several different combinations of features, obtained with feature selection algorithms or manually selected were tested. The performance of the solution was measured with 20 repetitions of 10-fold cross validation, achieving a F-measure of 47.2% with precision of 46.8% and recall of 47.6%.

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