The power spectral density (PSD) of the signal describes the power present in the signal as a function of frequency, per unit frequency.
Published in Chapter:
AI-Based Emotion Recognition
Mousami Prashant Turuk (Pune Institute of Computer Technology, India), Sreemathy R. (Pune Institute of Computer Technology, India), Shardul Sandeep Khandekar (Pune Institute of Computer Technology, India), and Soumya Sanjay Khurana (Pune Institute of Computer Technology, India)
Copyright: © 2023
|Pages: 21
DOI: 10.4018/978-1-7998-9220-5.ch049
Abstract
Behaviors, actions, pose, facial expressions, and speech are considered as channels that convey human emotions. Extensive research has been carried out to explore the relationships between these channels and emotions. The proposed method consists of a neural network-based solution combined with image processing and speech processing to classify the universal emotions: happy, anger, sad, and neutral. Speech processing includes extraction of spectral and temporal features like MFCC, energy, and then a set of values is given as input to the neural network. In image processing, Gabor filter texture features are used to extract a set of selected feature points. Mutual information is calculated and given as an input to the neural network for classification. The experimental results demonstrate the efficacy of audio-visual cues especially using few prominent features as overall accuracy of the combined approach is above 85%.