The coordination between the dancer's movement and the accompaniment of music is an important element of dance performance. In this paper, we propose a system for coordination evaluation of street dance. Given a dance video clip as input, the system first extracts motion beats from the video and then measures how well the motion beats correlate with the music beats. The motion beats are obtained by analyzing the speed and the change of direction of the dancer's movement. Unlike most previous work, which mainly focuses on 2D motion trajectory analysis, our system provides a more efficient and accurate dance movement analysis by using the 3D joint data of the dancer acquired by Kinect. Another distinction of our system is that, for beat correlation, it considers not only the underlying steady beat of music but also the groove pattern that gives the propulsive rhythmic feel of the music. We believe the differentiation of music beats into these two categories leads to a finer dance coordination evaluation. The test video clips for performance evaluation are generated by professional dancers. The average F -score of our system is about 80%.
An automatic method is presented for detecting myocardial ischemia, which can be considered as the early symptom of acute coronary events. Myocardial ischemia commonly manifests as ST- and T-wave changes on ECG signals. The methods in this study are proposed to detect abnormal ECG beats using knowledge-based features and classification methods. A novel classification method, sparse representation-based classification (SRC), is involved to improve the performance of the existing algorithms. A comparison was made between two classification methods, SRC and support-vector machine (SVM), using rule-based vectors as input feature space. The two methods are proposed with quantitative evaluation to validate their performances. The results of SRC method encompassed with rule-based features demonstrate higher sensitivity than that of SVM. However, the specificity and precision are a trade-off. Moreover, SRC method is less dependent on the selection of rule-based features and can achieve high performance using fewer features. The overall performances of the two methods proposed in this study are better than the previous methods.
A music piece consists of melody and accompaniment in many genres. In this paper, we present a system to automatically generate accompaniment that evokes specific emotions for a given melody. In particular, we propose harmonic progression and onset rate as two key features for emotion-based accompaniment generation. The former refers to the progression of chords, and the latter refers to the number of music events (such as notes and drums) in a unit time. The harmonic progression and the onset rate are altered according to the specified emotion represented by the valence and arousal parameters, respectively. The performance of the system is evaluated subjectively, and the result shows a perfect positive Spearman correlation between the specified emotion and the perceived emotion.
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