Affective response to tunes synthesized with musical pitch curves.

Tunes perceived as happy may help a user reach an affective state of positive valence. However, a user with negative valence may not be ready to listen to such a tune immediately. In this paper, we consider nudging a user from their current affective state to a target affective state in small steps.

Data-driven measurement of precision of components of pitch curves in Carnatic music.

Carnatic music (CM) is characterized by continuous pitch variations called gamakas, which are learned by example. Precision is measured on the points of zero-slope in gamaka- and non-gamaka-segments of the pitch curve as the standard deviation (SD) of the error in their pitch with respect to targets. Two previous techniques are considered to identify targets: the nearest semitone and the most likely mean of a semi-continuous Gaussian mixture model.

Detecting emotional valence using time-domain analysis of speech signals.

Mental health is a growing concern and its problems range from inability to cope with day-to-day stress to severe conditions like depression. Ability to detect these symptoms heavily relies on accurate measurements of emotion and its components, such as emotional valence comprising of positive, negative and neutral affect. Speech as a bio-signal to measure valence is interesting because of the ubiquity of smartphones that can easily record and process speech signals.

Spike Estimation from Fluorescence Signals Using High-Resolution Property of Group Delay.

Spike estimation from calcium (Ca) fluorescence signals is a fundamental and challenging problem in neuroscience. Several models and algorithms have been proposed for this task over the past decade. Nevertheless, it is still hard to achieve accurate spike positions from the Ca fluorescence signals.

EmoSense: Automatically Sensing Emotions From Speech By Multi-way Classification.

Reliably detecting emotions is a topic of current research in understanding mental health. Among the many modes of detecting emotion, audio has a prominent place. In this paper, we propose a two-level, multi-way classifier applied to classification of seven emotions from the standard Emo-DB database.

Comparing Manual and Machine Annotations of Emotions in Non-acted Speech.

Psychological well-being at the workplace has increased the demand for detecting emotions with higher accuracies. Speech, one of the most non-obtrusive modes of capturing emotions at the workplace, is still in need of robust emotion annotation mechanisms for non-acted speech corpora. In this paper, we extend our experiments on our non-acted speech database in two ways.