SMSAT: An Acoustic Dataset and Multi-Feature Deep Contrastive Learning Framework for Affective and Physiological Modeling of Spiritual Meditation

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.