Abstract
This study introduces a methodological framework for constructing virtual ironic environments through the deliberate mismatching of emotional profiles in music and imagery. We conducted a statistical analysis of "happy/joy" and "angry" samples from two independent datasets to identify significant acoustic and visual features. These feature profiles were translated into mid-level semantic prompts to guide AI-based generation of visual and musical content. Our findings reveal distinct emotional signatures: happy music exhibits higher rhythmic onset rates and greater spectral variability, whereas angry music is characterized by a higher spectral centroid and more stable dissonance. Visually, joyful images are brighter and more symmetrical, while angry images feature darker hues and concentrated color distributions. Furthermore, mid-level perceptual descriptors generate the most coherent content, and we employed them to build a spectrum of virtual environments, including Sarcastic (joyful visuals + angry music) and Kind Ironic (angry visuals + happy music) spaces. This work establishes a new, data-driven approach to affective computing and speculative virtual design, grounded in the formal principle of audiovisual dissonance.

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We present a novel physics-based concatenative sound synthesis (CSS) methodology for congruent interactions across physical, graphical, aural and haptic modalities in Virtual Environments. Navigation in aural and haptic corpora of annotated audio units is driven by user interactions with highly realistic photogrammetric based models in a game engine, where automated and interactive positional, physics and graphics data are supported. From a technical perspective, the current contribution expands existing CSS frameworks in avoiding mapping or mining the annotation data to real-time performance attributes, while guaranteeing degrees of novelty and variation for the same gesture.

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This study introduces and evaluates a new methodology for cross-cultural ethnomusicological analysis of symbolic music. We investigate music similarity in popular traditions rooted in oral transmission by identifying shared patterns at scale across multiple hierarchies. The novelty of our approach lies in expanding musical similarity phyloanalysis-typically adopting alignment metrics that compare entire scores-to structurally aware phrases and macrostructure (i.e., form) alignment. Additionally, we explore patterns derived from multiple representations (chromatic interval, diatonic interval, rhythmic ratios, and a combination of them) to facilitate the exploration of stylistic affinities across musical genres and traditions. Our method is tested on a new dataset of 600 Galician and Irish popular music scores, which includes expert annotations for 21 genres (four shared between the two traditions) and detailed phrase information, all made available as open-access data. We use the genre separation ratio to examine how alignment strategies capture stylistic structure, providing insights that support musicological exploration across genres and traditions. The resulting phylogenetic trees and distance matrices reveal relationships among traditions, genres, and scores, facilitating the exploration of cross-cultural influences and enabling the identification of shared patterns at multiple hierarchies.

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