Audio Analysis System for Neuro-Physiological Arousal Prediction
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Solution Overview
Problem
Existing methods for analyzing sound, such as music, to predict its effect on neuro-physiological arousal are unreliable due to subjectivity and inability to automatically detect musical parameters like tempo and harmonicity.
Innovation Solution
A computer-implemented system that analyzes sounds based on musical parameters derived from a predictive model of human neuro-physiological functioning, allowing for automatic selection and playback of sounds to manipulate arousal levels in listeners.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If subjective judgment methods are used to analyze music for arousal prediction, then expert opinion can be obtained, but reliability is poor due to subjectivity
Solution Approach 1:
The patent replaces subjective human judgment (mechanical/expert system) with an automated computer-based analysis system that processes acoustic features and spectral characteristics of music to objectively predict arousal levels, eliminating human subjectivity while maintaining reliability
Solution Approach 2:
The system enables self-service by allowing the computer to automatically analyze music tracks and predict arousal effects without requiring expert human intervention, using automated feature extraction and pattern recognition algorithms
2Extent of automation
If automated tempo analysis is used to predict arousal, then objectivity is improved, but accuracy is limited because tempo alone is insufficient
Solution Approach 1:
The patent merges multiple analysis dimensions including tempo, spectral flux, harmonicity, and other acoustic features into a comprehensive arousal prediction model, combining various automated analysis techniques to achieve precise predictions that tempo alone cannot provide
Solution Approach 2:
The system uses a composite approach by integrating multiple feature types (temporal, spectral, harmonic) into a unified analysis framework, similar to composite materials, where each feature contributes unique information that enhances overall prediction accuracy
3Reliability
If multiple musical parameters are analyzed to improve prediction accuracy, then reliability improves, but computational complexity increases
Solution Approach 1:
The patent segments the music analysis into distinct feature extraction modules (tempo detection, spectral analysis, harmonicity measurement) that can be processed independently and then integrated, managing computational complexity through modular organization of multiple analysis parameters
Data Source
AI summary
The present invention relates to a method and system for analysing audio (eg. music) tracks. A predictive model of the neuro-physiological functioning and response to sounds by one or more of the human lower cortical, limbic and subcortical regions in the brain is described. Sounds are analysed so that appropriate sounds can be selected and played to a listener in order to stimulate and/or manipulate neuro-physiological arousal in that listener. The method and system are particularly applicable to applications harnessing a biofeedback resource.