[month] [year]

Sharon Maria Varghese

Sharon Maria Varghese supervised by Dr. Vinoo A R received her Master of Science in Computer Science and Engineering (CSE). Here’s a summary of her research work on Tailored Music Recommendations for Individuals with Autism Spectrum Disorder: A Goal-Driven Approach:

Music has therapeutic potential for individuals with Autism Spectrum Disorder (ASD) (Sharda et al., 2018; Geretsegger et al., 2014). Traditional music therapy often lacks personalization, with music selected by therapists rather than tailored to individual preferences. Current studies on ASD individuals’ music preferences often overlook cultural inclusivity and the impact of lyrics (McFerran & Shoemark, 2013). Moreover, existing recommendation systems do not consider music preferences tailored for therapeutic goals. This thesis explores how music aids emotional regulation, cognitive functioning, and social interaction in individuals with ASD, emphasizing cultural inclusivity. The study includes analyses of music-specific communities for neurotypical (NT) and ASD individuals on Reddit, cross-cultural comparisons between non-Indian and Indian populations, a pilot study with Indian children with ASD, and the development of a personalized Music Recommendation System. Reddit analysis of music-specific communities revealed diverse music preferences, underscoring the need for personalized interventions. Cross-cultural comparisons showed that nonIndian participants preferred Rock and Western Classical music, while Indian participants favoured Indian film and Devotional music, reflecting cultural influences on emotional expression (Rentfrow & Gosling, 2003). The pilot study with Indian children identified diverse music preferences, including Indian Devotional music, Nursery rhymes, and Indian film music. Listening to preferred music indicated positive behavioural changes, such as reduced anxiety and improved social interaction, highlighting music’s therapeutic potential (Gold, Voracek, & Wigram, 2004). The GOAL (Genre, Optimization, Acoustic Features, and Lyrics) based Music Recommendation System offers personalized suggestions based on genres, acoustic features, and lyrical themes relevant to emotional regulation, cognitive functioning, and social skills. By categorizing goals into emotional, cognitive, and social functions, the system aligns with diverse ASD needs and provides personalized music recommendations using similarity metrics. Evaluation metrics indicated the model accurately recommends relevant music, achieving a high proportion of liked music (precision) and capturing a significant portion of preferred music (recall). Behavioural changes showed effectiveness in mood management, focus, sensory load regulation, and sleep regularity, with mixed results in communication and social skills. In conclusion, this thesis underscores the importance of recognizing and accommodating ASD individuals’ music preferences in therapy. Despite limitations such as small sample sizes and subjective reports, the findings offer valuable insights into music’s therapeutic potential for ASD. Future research should include larger, diverse samples and longitudinal studies to enhance the proposed system’s effectiveness (Geretsegger et al., 2014).

November 2024