SMSAT: An Acoustic Dataset and Multi-Feature Deep Contrastive Learning Framework for Affective and Physiological Modeling of Spiritual Meditation
Ahmad Suleman, Yazeed Alkhrijah, Misha Urooj Khan, Hareem Khan, Muhammad Abdullah Husnain Ali Faiz, Mohamad A. Alawad, Zeeshan Kaleem, Guan Gui
IEEE Transactions on Affective Computing April 1, 2026 DOI: 10.1109/taffc.2026.3653301 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational cohort Peer reviewed |
|---|---|
| Topics | Meditation |
| Key findings | The proposed models achieved 99% accuracy in three-stimulus classification, with subject-wise evaluation yielding 98.4% for CAM and 96.5% for the SMSAT encoder. Spiritual meditation showed stronger effects on cardiac response characteristics compared to music and natural silence, indicating significant physiological differences across auditory conditions. |
Abstract
Auditory stimuli strongly shape emotional and physiological states, making them central to affective computing and mental health technologies.We present the study of three auditory conditions, spiritual meditation (SM), music (M), and natural silence (NS), using acoustic time-series signals. To support this, we introduce the Spiritual, Music, Silence Acoustic Time Series (SMSAT) dataset, a benchmark of controlled acoustic recordings with demographic diversity.We develop a contrastive learning-based SMSAT encoder that learns discriminative embeddings from ATS data, achieving 99% accuracy. In addition, we propose the Calmness Analysis Model (CAM), integrating multi-domain features for affective state classification, achieving a 99% accuracy in the three-stimulus classification task. Inter & intra-class feature space separability, calmness evaluation using Temporal Segmented Response Profiling (TSRP) confirm significant physiological differences across auditory conditions, with SM showing stronger effects on cardiac response characteristics (CRC).WaveGAN is used to generate additional dataset. Under subject-wise evaluation, CAM reached 98.4% accuracy, and the SMSAT Encoder achieved 96.5% accuracy. This work provides a validated dataset and scalable deep learning framework for stress monitoring, well-being, and therapeutic audio interventions.
Comparable studies
Other observational and cohort studies on meditation, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Using Self-Report Assessment Methods to Explore Facets of Mindfulness Undergraduate students | 2006 | Observational cohort | |
| Effectiveness of a meditation-based stress reduction program in the treatment of anxiety disorders Patients with generalized anxiety disorder or panic disorder with or without agoraphobia | 1992 | Observational cohort | n = 22 |
| Meditation experience is associated with increased cortical thickness Participants with extensive Insight meditation experience and matched controls | 2005 | Observational cohort | n = 20 |
| Meditation experience is associated with differences in default mode network activity and connectivity Experienced meditators and matched meditation-naive controls | 2011 | Observational cohort | |
| Long-term meditators self-induce high-amplitude gamma synchrony during mental practice Long-term Buddhist practitioners and controls | 2004 | Observational cohort |