Fractal Dimension Pattern Based Multiresolution Analysis for Rough Estimator of Person-Dependent Audio Emotion Recognition

TL;DR

This paper introduces a novel multiresolution analysis method using fractal dimension patterns to improve person-dependent audio emotion recognition, capturing intrinsic auditory emotional features for better classification.

Contribution

It proposes a new approach combining fractal dimension features with multiresolution analysis for more effective person-dependent audio emotion recognition.

Findings

Achieved comparable performance with existing methods

Effectively captures intrinsic emotional features from audio signals

Demonstrated robustness across different speakers

Abstract

As a general means of expression, audio analysis and recognition has attracted much attentions for its wide applications in real-life world. Audio emotion recognition (AER) attempts to understand emotional states of human with the given utterance signals, and has been studied abroad for its further development on friendly human-machine interfaces. Distinguish from other existing works, the person-dependent patterns of audio emotions are conducted, and fractal dimension features are calculated for acoustic feature extraction. Furthermore, it is able to efficiently learn intrinsic characteristics of auditory emotions, while the utterance features are learned from fractal dimensions of each sub-bands. Experimental results show the proposed method is able to provide comparative performance for audio emotion recognition.