Abstract
The extraction of harmonic information from musical audio is fundamental for several music information retrieval tasks. In this paper, we propose novel harmonic audio features based on the perceptually-inspired tonal interval vector space, computed as the Fourier transform of chroma vectors. Our contribution includes mid-level features for musical dissonance, chromaticity, dyadicity, triadicity, diminished quality, diatonicity, and whole-toneness. Moreover, we quantify the perceptual relationship between short- and long-term harmonic structures, tonal dispersion, harmonic changes, and complexity. Beyond the computation on fixed-size windows, we propose a context-sensitive harmonic segmentation approach. We assess the robustness of the new harmonic features in style classification tasks regarding classical music periods and composers. Our results align with, slightly outperforming, existing features and suggest that other musical properties than those in state-of-the-art literature are partially captured. We discuss the features regarding their musical interpretation and compare the different feature groups regarding their effectiveness for discriminating classical music periods and composers. © F. Almeida, G. Bernardes, and C. Weiû.

Abstract
Silent motors are an excellent strategy to combat noise pollution. Still, they can pose risks for pedestrians who rely on auditory cues for safety and reduce driver awareness due to the absence of the familiar sounds of combustion engines. Sound design for silent motors not only tackles the above issues but goes beyond safety standards towards a user-centered approach by considering how users perceive and interpret sounds. This paper examines the evolving field of sound design for electric vehicles (EVs), focusing on Acoustic Vehicle Alerting Systems (AVAS). The study analyzes existing AVAS, classifying them into different groups according to their design characteristics, from technical concerns and approaches to aesthetic properties. Based on the proposed classification, an (adaptive) sound design methodology, and concept for AVAS are proposed based on state-of-the-art technologies and tools (APIs), like Wwise Automotive, and integration through a functional prototype within a virtual environment. We validate our solution by conducting user tests focusing on EV sound perception and preferences in rural and urban environments. Results showed participants preferred nature-like and melodic sounds with a wide range of frequencies, emphasizing 1000Hz, in rural areas, for the AVAS. For the interior experience, melodic, reliable, and relaxing sounds with a frequency range from 200Hz to 500Hz. In urban areas, melodic, futuristic, but not overpowering sounds (80Hz to 700Hz) with balanced frequencies at high speeds were chosen for the car's exterior. In the interior, melodic, futuristic, and combustion engine-like sounds with a low frequencies background and higher frequencies at high speeds were also preferred. © 2025 SAE International. All Rights Reserved.

Abstract
This paper describes an approach to algorithmic music composition that takes narrative structures as input, allowing composers to create music directly from narrative elements. Creating narrative development in music remains a challenging task in algorithmic composition. Our system addresses this by combining leitmotifs to represent characters, generative grammars for harmonic coherence, and evolutionary algorithms to align musical tension with narrative progression. The system operates at different scales, from overall plot structure to individual motifs, enabling both autonomous composition and co-creation with varying degrees of user control. Evaluation with compositions based on tales demonstrated the system's ability to compose music that supports narrative listening and aligns with its source narratives, while being perceived as familiar and enjoyable. © 2025 International Joint Conferences on Artificial Intelligence. All rights reserved.

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.