Abstract
Folk music and particularly children's folk songs serve as vital repositories of cultural identity, emotional expression, and social values. This study presents a computational thematic analysis of Portuguese and Spanish children's folk songs using the I-Folk corpus, comprising 800 annotated entries in the Music Encoding Initiative (MEI) format. Despite shared historical influences on the Iberian Peninsula, the lyrical content of each tradition reveals distinct thematic orientations. Through a methodological framework that combines traditional text pre-processing, frequency analysis, and semantic embedding using large language models (LLMs), we uncover cross-cultural similarities and divergences in content, form, and emotional register. Spanish lyrics focus primarily on caregiving, emotional development, and moral-religious motifs, while Portuguese songs emphasize performative rhythm, localized identity, and folkloric references. Our results highlight the need for tailored analytical strategies when working with children's repertoire and demonstrate the utility of LLMs in capturing culturally embedded patterns that are often obscured in conventional analyses. This work contributes to digital folklore scholarship, corpus-based ethnomusicology, and the preservation of underrepresented cultural expressions in computational humanities.

Abstract
We present an analysis of performance configurations in Portuguese traditional music, using computational methods to process field recordings from the A Musica Portuguesa A Gostar Dela Propria (MPAGDP) archive. Our approach employs YOLOv11s (You Only Look Once), a computer vision system that can detect and count performers in archival footage, allowing us to automatically classify performances into meaningful categories: solo, duo, small, and large ensembles. This computational classification method processed 8122 field recordings with 96% classification accuracy, enabling systematic examination of performance contexts that would be time-consuming through manual analysis. Our analysis shows relationships between performance configuration and musical practice across Portuguese traditions. Solo performers, comprising 48% of vocal recordings, predominantly appear in narrative and poetic traditions requiring individual expression. Large ensembles (21%) maintain collective practices like polyphonic singing traditions. The geographic distribution shows regional traits-Alentejo features large-ensemble singing traditions, while northern regions favor solo performances. The temporal analysis traces how traditional forms maintain continuity through specific performance configurations, while contemporary adaptations emerge primarily in small group formats, illuminating the social dimensions of musical transmission and adaptation in Portuguese traditional music.

Abstract
This article investigates sampling strategies in latent space navigation to enhance co-creative music systems, focusing on timbre latent spaces. Adopting Villa-Rojo's 'Lamento' for tenor saxophone and tape as a case study, we conducted two experiments. The first assessed traditional corpus-based concatenative synthesis sampling within the RAVE model's latent space, finding that sampling strategies gradually deviate from a given target sonority while still relating to the original morphology. The second experiment aims at defining sampling strategies for creating variations of an input signal, namely parallel, contrary, and oblique motions. The findings expose the need to explore individual model layers and the geometric transformation nature of the contrary and oblique motions that tend to dilate the original shape. The findings highlight the potential of motion-aware sampling for more contextually aware and expressive control of music structures via CBCS.

Abstract
Music production has long been characterized by well-defined concepts and techniques. However, a notable gap exists in applying these established principles to music production within immersive media. This paper addresses this gap by examining post-production processes applied to three case studies, i.e., three songs with unique instrumental features and narratives. The primary objective is to facilitate an in-depth analysis of technical and artistic challenges in musical production for immersive media. From a detailed analysis of technical and artistic post-production decisions in the three case studies and a critical examination of theories and techniques from sound design and music production, we propose a framework with a tripartite mixing categorization for immersive media: Traditional Production, Expanded Traditional Production, and Nontraditional Production. These concepts expand music production methodologies in the context of immersive media, offering a framework for understanding the complexities of spatial audio. By exploring these interdisciplinary connections, we aim to enrich the discourse surrounding music production, rethinking its conceptual plane into more integrative media practices outside the core music production paradigm, thus contributing to developing innovative production methodologies. © 2025 IEEE.

Abstract
Building on theories of human sound perception and spatial cognition, this paper introduces a sonification method that facilitates navigation by auditory cues. These cues help users recognize objects and key urban architectural elements, encoding their semantic and spatial properties using non-speech audio signals. The study reviews advances in object detection and sonification methodologies, proposing a novel approach that maps semantic properties (i.e., material, width, interaction level) to timbre, pitch, and gain modulation and spatial properties (i.e., distance, position, elevation) to gain, panning, and melodic sequences. We adopt a three-phase methodology to validate our method. First, we selected sounds to represent the object’s materials based on the acoustic properties of crowdsourced annotated samples. Second, we conducted an online perceptual experiment to evaluate intuitive mappings between sounds and object semantic attributes. Finally, in-person navigation experiments were conducted in virtual reality to assess semantic and spatial recognition. The results demonstrate a notable perceptual differentiation between materials, with a global accuracy of .69 ± .13 and a mean navigation accuracy of .73 ± .16, highlighting the method’s effectiveness. Furthermore, the results suggest a need for improved associations between sounds and objects and reveal demographic factors that are influential in the perception of sounds.

Abstract
Music therapy has emerged as a promising approach to support various mental health conditions, offering non-pharmacological therapies with evidence of improved well-being. Rapid advancements in artificial intelligence (AI) have recently opened new possibilities for ‘personalized’ musical interventions in mental health care. This article explores the application of AI in the context of mental health, focusing on the use of machine learning (ML), deep learning (DL), and generative music (GM) to personalize musical interventions. The methodology included a scoping review in the Scopus and PubMed databases, using keywords denoting emerging AI technologies, music-related contexts, and application domains within mental health and well-being. Identified research lines encompass the analysis and generation of emotional patterns in music using ML, DL, and GM techniques to create musical experiences adapted to user needs. The results highlight that these technologies effectively promote emotional and cognitive well-being, enabling personalized interventions that expand mental health therapies.