Musical Emotion Recognition with Spectral Feature Extraction based on a Sinusoidal Model with Model-based and Deep-learning approaches.

This paper presents a method for extracting novel spectral features based on a sinusoidal model. The method is focused on characterizing the spectral shapes of audio signals using spectral peaks in frequency sub-bands. The extracted features are evaluated for predicting the levels of emotional dimensions, namely arousal and valence.

An examination of the startle response during upper limb stretch perturbations.

Unexpected presentation of a startling auditory stimulus (SAS>120 decibels) in a reaction time (RT) paradigm results in the startle reflex and an early release (<100ms) of the preplanned motor response (StartReact effect). Mechanical perturbations applied to the upper limbs elicit short- (M1) and long-latency (M2) stretch reflexes and have also been shown to initiate intended motor responses early (<100ms). Ravichandran et al.

Speech intelligibility estimation using multi-resolution spectral features for speakers undergoing cancer treatment.

Head and neck cancer can significantly hamper speech production which often reduces speech intelligibility. A method of extracting spectral features is presented. The method uses a multi-resolution sinusoidal transform scheme, which enables better representation of spectral and harmonic characteristics.