Activating and relaxing music entrains the speed of beat synchronized walking.

Inspired by a theory of embodied music cognition, we investigate whether music can entrain the speed of beat synchronized walking. If human walking is in synchrony with the beat and all musical stimuli have the same duration and the same tempo, then differences in walking speed can only be the result of music-induced differences in stride length, thus reflecting the vigor or physical strength of the movement. Participants walked in an open field in synchrony with the beat of 52 different musical stimuli all having a tempo of 130 beats per minute and a meter of 4 beats. The walking speed was measured as the walked distance during a time interval of 30 seconds. The results reveal that some music is 'activating' in the sense that it increases the speed, and some music is 'relaxing' in the sense that it decreases the speed, compared to the spontaneous walked speed in response to metronome stimuli. Participants are consistent in their observation of qualitative differences between the relaxing and activating musical stimuli. Using regression analysis, it was possible to set up a predictive model using only four sonic features that explain 60% of the variance. The sonic features capture variation in loudness and pitch patterns at periods of three, four and six beats, suggesting that expressive patterns in music are responsible for the effect. The mechanism may be attributed to an attentional shift, a subliminal audio-motor entrainment mechanism, or an arousal effect, but further study is needed to figure this out. Overall, the study supports the hypothesis that recurrent patterns of fluctuation affecting the binary meter strength of the music may entrain the vigor of the movement. The study opens up new perspectives for understanding the relationship between entrainment and expressiveness, with the possibility to develop applications that can be used in domains such as sports and physical rehabilitation.

A practical approach to model selection for support vector machines with a Gaussian kernel.

When learning a support vector machine (SVM) from a set of labeled development patterns, the ultimate goal is to get a classifier attaining a low error rate on new patterns. This so-called generalization ability obviously depends on the choices of the learning parameters that control the learning process. Model selection is the method for identifying appropriate values for these parameters. In this paper, a novel model selection method for SVMs with a Gaussian kernel is proposed. Its aim is to find suitable values for the kernel parameter γ and the cost parameter C with a minimum amount of central processing unit time. The determination of the kernel parameter is based on the argument that, for most patterns, the decision function of the SVM should consist of a sufficiently large number of significant contributions. A unique property of the proposed method is that it retrieves the kernel parameter as a simple analytical function of the dimensionality of the feature space and the dispersion of the classes in that space. An experimental evaluation on a test bed of 17 classification problems has shown that the new method favorably competes with two recently published methods: the classification of new patterns is equally good, but the computational effort to identify the learning parameters is substantially lower.