collaborators

8 papers

cs.HC2026

Cross-cultural evaluation of taste-sound correspondences in AI-generated music

Matteo Spanio, Massimiliano Zampini, Luisa Torri +6

Sonic seasoning research has shown that listeners attribute systematic gustatory and emotional meaning to sound, and text-to-music generative artificial intelligence has recently b…

cs.SD2026

A Quantized Native Runtime for On-Device Semantic Audio Generation

Matteo Spanio, Antonio RodÃ

Semantic audio applications increasingly require controllable generation on commodity and embedded hardware rather than through framework-heavy datacenter stacks. We present \texti…

cs.SD2026

Taste-aware music retrieval from audio embeddings

Matteo Spanio, Antonio RodÃ

Crossmodal correspondences between sound and taste are well established in psychology and neuroscience, but largely absent from content-based multimedia retrieval. We formalise tas…

cs.SD2026

Can LLMs understand LilyPond? A benchmark for symbolic music generation and understanding

Matteo Spanio, Mohammad Torabi, Andrea Poltronieri +1

Symbolic music evaluation for large language models remains fragmented across representations, datasets, and metrics. We introduce LilyBench, a LilyPond-based benchmark that jointl…

cs.SD2026

BMdataset: A Musicologically Curated LilyPond Dataset

Matteo Spanio, Ilay Guler, Antonio RodÃ

Symbolic music research has relied almost exclusively on MIDI-based datasets; text-based engraving formats such as LilyPond remain unexplored for music understanding. We present BM…

cs.SD2026

Multimodal Dataset Normalization and Perceptual Validation for Music-Taste Correspondences

Matteo Spanio, Valentina Frezzato, Antonio RodÃ

Collecting large, aligned cross-modal datasets for music-flavor research is difficult because perceptual experiments are costly and small by design. We address this bottleneck thro…