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We investigated the possibility of using a machine-learning scheme in conjunction with commercial wearable EEG-devices for translating listeners subjective experience of music into scores that can be used for the automated annotation of music in popular on-demand streaming services. Based on the established -neuroscientifically sound- concepts of brainwave frequency bands, activation asymmetry index and cross-frequency-coupling (CFC), we introduce a Brain Computer Interface (BCI) system that automatically assigns a rating score to the listened song. Our research operated in two distinct stages: i) a generic feature engineering stage, in which features from signal-analytics were ranked and selected based on their ability to associate music induced perturbations in brainwaves with listeners appraisal of music. ii) a personalization stage, during which the efficiency of ex- treme learning machines (ELMs) is exploited so as to translate the derived pat- terns into a listeners score. Encouraging experimental results, from a pragmatic use of the system, are presented.
We investigated the possibility of using a machine-learning scheme in conjunction with commercial wearable EEG-devices for translating listeners subjective experience of music into scores that can be used in popular on-demand music streaming services
Graphical passwords have been demonstrated to be the possible alternatives to traditional alphanumeric passwords. However, they still tend to follow predictable patterns that are easier to attack. The crux of the problem is users memory limitations.
We present a new approach to harmonic analysis that is trained to segment music into a sequence of chord spans tagged with chord labels. Formulated as a semi-Markov Conditional Random Field (semi-CRF), this joint segmentation and labeling approach en
Objective evaluation (OE) is essential to artificial music, but its often very hard to determine the quality of OEs. Hitherto, subjective evaluation (SE) remains reliable and prevailing but suffers inevitable disadvantages that OEs may overcome. Ther
Recent advances in biosensors technology and mobile electroencephalographic (EEG) interfaces have opened new application fields for cognitive monitoring. A computable biomarker for the assessment of spontaneous aesthetic brain responses during music