The evolution of rhythmic cognition: New perspectives and technologies in comparative research

Music is a pervasive phenomenon in human culture, and musical rhythm is virtually present in all musical traditions. Research on the evolution and cognitive underpinnings of rhythm can benefit from a number of approaches. We outline key concepts and definitions, allowing fine-grained analysis of rhythmic cognition in experimental studies. We advocate comparative animal research as a useful approach to answer questions about human music cognition and review experimental evidence from different species. Finally, we suggest future directions for research on the cognitive basis of rhythm. Apart from research in semi-natural setups, possibly allowed by “drum set for chimpanzees” prototypes presented here for the first time, mathematical modeling and systematic use of circular statistics may allow promising advances

The Appreciative Heart: The Psychophysiology of Positive Emotions and Optimal Functioning

This monograph is an overview of Institute of HeartMath's research on the physiological correlates of positive emotions and the science underlying two core HeartMath techniques which supports Heart-Based Living. The heart's connection with love and other positive emotions has survived throughout millennia and across many diverse cultures. New empirical research is providing scientific validation for this age-old association. This 21-page monograph offers a comprehensive understanding of the Institute of HeartMath's cutting-edge research exploring the heart's central role in emotional experience. Described in detail is physiological coherence, a distinct mode of physiological functioning, which is generated during sustained positive emotions and linked with beneficial health and performance-related outcomes. The monograph also provides steps and applications of two HeartMath techniques, Freeze-Frame(R) and Heart Lock-In(R), which engage the heart to help transform stress and produce sustained states of coherence. Data from outcome studies are presented, which suggest that these techniques facilitate a beneficial repatterning process at the mental, emotional and physiological levels

Improving music genre classification using automatically induced harmony rules

We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates