Information Retrieval (IR) increasingly relies on subjective, graded, and natural-language notions of relevance, motivating the development of reproducible test collections for ranking and recommendation. In affect-sensitive music domains, however, such resources with human-validated relevance signals remain scarce. We introduce CalmSet, a test collection for emotion-tagged music retrieval and recommendation in a therapeutic context for children with Autism Spectrum Disorder (ASD). CalmSet contains 432 modular music tracks instantiated from four purposefully composed base songs with controlled provenance, each formed by distinct combinations of seven active musical layers. Each track is annotated with ranked top-3 therapeutic intent labels and natural-language descriptions. Annotations are produced via a hybrid human-in-the-loop pipeline: CLAP proposes candidate intent labels, a large language model generates auxiliary semantic descriptions, and crowd workers provide ranked judgments without exposure to model outputs; final labels are aggregated using a Borda-based procedure. As initial baselines, we evaluate five one-vs-rest multi-label classifiers over CLAP audio embeddings, observing moderate micro-F1 scores (up to 0.60) but low exact-match accuracy (<0.10), while top-3 label overlap is substantially higher (Jaccard@3 up to 0.48), motivating graded-relevance evaluation. CalmSet supports both sparse (e.g., BM25) and dense audio–text retrieval models using therapeutic labels or natural-language descriptions as queries.

Children with Autism Spectrum Disorder (ASD) often face challenges with musical engagement due to unique sensory and neural processing needs.To address this, we introduce uCue, a musical interface designed to enhance active musical engagement.This interactive playback system offers modular arrangements of children's songs at an accessible tempo, enabling listeners to manipulate musical layers and create personalized renditions in real-time.Deployed in listening sessions with seven parent-child dyads, uCue facilitated self-expression through singing and gestures while fostering emotional regulation and sensory engagement.Participants quickly adapted to the interface, preferred soothing sounds, and expressed interest in more rhythmic layers over time.Our findings suggest that uCue has the potential to enhance musical interactions by allowing children to explore and control auditory experiences.We also discuss how uCue and data from its logs might support therapeutic goals in music therapy for children with ASD and provide design recommendations for similar technologies.

Large Language Models (LLMs) can analyze large datasets semantically. However, research on applying LLMs for mental health text classification is relatively new and developing. Existing methods often use supervised, deep, and reinforcement learning, which rely heavily on fine-tuning and reward models. To investigate whether LLMs can assist in recommending mental health apps based on user reviews, our study collected approximately 200k user reviews from 73 mental health mobile applications. We instructed selected LLMs to classify individual reviews into 1-5 star ratings, subsequently averaging these results to derive an overall rating for each app reflecting current user feedback. While the best supervised learning method in our experiments achieved an F1-Score of 0.79 which required significantly more human effort, the GPT-4 and Gemini 1.5 Pro delivered a strong ‘out-of-the-box’ performance with an overall F1-Score of 0.76. We provide further statistical comparisons and discussions of the performance of these models for the text classification task. Using a crowdsourcing platform to determine agreement levels, we observed that human ratings align closely with GPT ratings. In addition, we analyze specific features and concerns highlighted in mental health app reviews. Alongside our analysis, we make our data available for further experimentation and benchmarking.