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

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.

Abstract
State-of-the-art symbolic music generation models have recently achieved remarkable output quality, yet explicit control over compositional features, such as tonal tension, remains challenging. We propose a novel approach that integrates a computational tonal tension model, based on tonal interval vector analysis, into a Transformer framework. Our method employs a two-level beam search strategy during inference. At the token level, generated candidates are re-ranked using model probability and diversity metrics to maintain overall quality. At the bar level, a tension-based re-ranking is applied to ensure that the generated music aligns with a desired tension curve. Objective evaluations indicate that our approach effectively modulates tonal tension, and subjective listening tests confirm that the system produces outputs that align with the target tension. These results demonstrate that explicit tension conditioning through a dual-level beam search provides a powerful and intuitive tool to guide AI-generated music. Furthermore, our experiments demonstrate that our method can generate multiple distinct musical interpretations under the same tension condition.

Abstract
We present a metadata enrichment framework for Music Encoding Initiative (MEI) files, featuring mid- to higher-level multimodal features to support content-driven (similarity) retrieval with semantic awareness across large collections. While traditional metadata captures basic bibliographic and structural elements, it often lacks the depth required for advanced retrieval tasks that rely on musical phrases, form, key or mode, idiosyncratic patterns, and textual topics. To address this, we propose a system that fosters the computational analysis and edition of MEI encodings at scale. Inserting extended metadata derived from computational analysis and heuristic rules lays the groundwork for more nuanced retrieval tools. A batch environment and a lightweight JavaScript web-based application propose a complementary workflow by offering large-scale annotations and an interactive environment for reviewing, validating, and refining MEI files' metadata. Development is informed by user-centered methodologies, including consultations with music editors and digital musicologists, and has been co-designed in the context of orally transmitted folk music traditions, ensuring that both the batch processes and interactive tools align with scholarly and domain-specific needs.