Juan Sebastián Gómez-Cañón | Challenges And Opportunities For Human-Centered Music Emotion Recognition
Music is intertwined with human emotion, memory, and identity, making it a powerful medium for affective experience and regulation.
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Music is intertwined with human emotion, memory, and identity, making it a powerful medium for affective experience and regulation.
What does digital inclusion look like in the age of AI? Over 6,000 of the world’s 7,000-plus living languages remain digitally disadvantaged.

What does digital inclusion look like in the age of AI? Over 6,000 of the world’s 7,000-plus living languages remain digitally disadvantaged.
The AI Index, currently in its ninth year, tracks, collates, distills, and visualizes data relating to artificial intelligence.

The AI Index, currently in its ninth year, tracks, collates, distills, and visualizes data relating to artificial intelligence.
Sequence data is ubiquitous in economics — job histories in labor economics, diagnosis and treatment sequences in health economics, strategic interactions in game theory. Generative sequence models can learn to predict these sequences well, but their complexity makes it hard to extract interpretable economic insights from their predictions.
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Sequence data is ubiquitous in economics — job histories in labor economics, diagnosis and treatment sequences in health economics, strategic interactions in game theory. Generative sequence models can learn to predict these sequences well, but their complexity makes it hard to extract interpretable economic insights from their predictions.
This has motivated decades of research in music emotion recognition (MER), aiming to model emotional responses to music using computational methods. However, emotional responses to music are not fixed properties of the signal but emerge from interactions between musical structure, listener background, cultural context, and situational factors. As a result, traditional MER approaches that rely on averaged labels or universal ground truth struggle to capture the diversity and subjectivity of emotional experiences.
This talk argues for a human-centered perspective on MER that treats subjectivity not as noise but as a core signal. I discuss methodological challenges in constructing meaningful ground truth, including inter-annotator disagreement, contextual dependence, and personalization, as well as ethical concerns related to the use of emotion in a political context, and potential misuse.