Jim Tørresen

Jim Tørresen

Professor, Department for Informatics, University of Oslo

MishMash role: Member · WP1 , WP3 , WP7

Informatics

Other projects

Latest results

Type

Book chapter

  1. Book chapter, 2026

    Reducing Robot Vulnerabilities Through Joint Regulatory Assessment and User-Centered Development and Testing

    Jim Tørresen ; Diana Saplacan Lindblom ; Adel Baselizadeh ; Tobias Mahler ; Lee Andrew Bygrave

  2. Book chapter, 2026

    Robots as Welfare Technologies and Actors within Future Homeand Healthcare Services

    Diana Saplacan Lindblom ; Sanna Kuoppamäki ; Leon Bodenhagen ; Jim Tørresen

  3. Book chapter, 2026

    A Cross-Cultural Video-Based Study between Norway and Japan on Informed Consent and Disclosure Mechanisms on the Use of Social Robots in Public and Private Spaces - Users' Perspectives

    Diana Saplacan Lindblom ; Yueh-Hsuan Weng ; Pedro Pablo Lucas Bravo ; Adel Baselizadeh ; Jim Tørresen

  4. Book chapter, 2026

    "There’s something human about it!” - Exploring users’ perception of nonverbal communication of robotic furniture

    Claudia Magdalena Sikora ; Marieke van Otterdijk ; Rebekka Soma-Jestilä ; Jim Tørresen ; Diana Saplacan Lindblom

Journal article

  2. Journal article, 2026

    Investigating Auditory–Visual Perception Using Multi-Modal Neural Networks with the SoundActions Dataset

    Open access CC BY Read it

    Jinyue Guo ; Jim Tørresen ; Alexander Refsum Jensenius

    Musicologists, psychologists, and computer scientists study relationships between auditory and visual stimuli from very different perspectives and using various terminologies and methodologies. This article aims to bridge the gap between phenomenological sound theory, auditory–visual theory, and audio–video processing and machine learning. We introduce the SoundActions dataset, a collection of 365 audio–video recordings of (primarily) short sound actions. Each recording has been human‑labeled and annotated according to Pierre Schaeffer’s theory of reduced listening, which describes the property of the sound itself (e.g., ‘an impulsive sound’) instead of the source (e.g., ‘a bird sound’). With these reduced‑type labels in the audio–video dataset, we conducted two experiments: (1) fine‑tuning the latest audio–video transformer model on the reduced‑type labels in the SoundActions dataset, proving that the model can recognize reduced‑type labels, and observing that the modality‑imbalance phenomenon is similar to the added value theory by Michel Chion and (2) proposing the Ensemble of Perception Mode Adapters method inspired by Pierre Schaeffer’s three listening modes, improving the audio–video model also on reduced‑type tasks.

More results in NVA…