Article: Intelligent multimodal analysis of musical time-series: an approach to mental health estimation Journal: International Journal of Reasoning-based Intelligent Systems (IJRIS) 2026 Vol.18 No.15 pp.31 - 43 Abstract: The reliance on subjective self-reports in mental health assessment poses significant challenges regarding objectivity and timeliness. This study introduces an approach based on the intelligent multimodal analysis of musical time-series to mitigate these issues. Unlike conventional techniques that ignore the temporal dynamics of emotional response, our framework models the continuous interplay between music audio and physiological markers (such as heart rate) to decode evolving psychological states. We formulate a dynamic analysis mechanism that specifically targets the subtle patterns of emotion during music perception. Empirical results show significant improvements over static baselines, yielding an accuracy exceeding 85% and an AUC of 0.91 in detecting depressive tendencies. These findings validate the efficacy of analysing musical time-series as a robust, non-invasive method for mental health estimation. Inderscience Publishers - linking academia, business and industry through research

Title: Intelligent multimodal analysis of musical time-series: an approach to mental health estimation

Authors: Ying Lv; Jing Wang

Addresses: School of Music, Jiangxi Normal University, Nanchang, 330027, China ' School of Music, Jiangxi Normal University, Nanchang, 330027, China

Abstract: The reliance on subjective self-reports in mental health assessment poses significant challenges regarding objectivity and timeliness. This study introduces an approach based on the intelligent multimodal analysis of musical time-series to mitigate these issues. Unlike conventional techniques that ignore the temporal dynamics of emotional response, our framework models the continuous interplay between music audio and physiological markers (such as heart rate) to decode evolving psychological states. We formulate a dynamic analysis mechanism that specifically targets the subtle patterns of emotion during music perception. Empirical results show significant improvements over static baselines, yielding an accuracy exceeding 85% and an AUC of 0.91 in detecting depressive tendencies. These findings validate the efficacy of analysing musical time-series as a robust, non-invasive method for mental health estimation.

Keywords: mental health estimation; multimodal analysis; musical time-series; physiological signals; depression detection.

DOI: 10.1504/IJRIS.2026.154119

International Journal of Reasoning-based Intelligent Systems, 2026 Vol.18 No.15, pp.31 - 43

Received: 06 Feb 2026
Accepted: 16 Apr 2026
Published online: 12 Jun 2026 *

Title: Intelligent multimodal analysis of musical time-series: an approach to mental health estimation

Keep up-to-date