Sylvain Argentieri

About

Sylvain Argentieri is a prominent robotics researcher whose work sits at the intersection of acoustic signal processing, robotic audition, and developmental robotics. Best known for his authoritative 2015 survey on sound source localization in robotics — which has accumulated nearly 150 citations and remains a foundational reference in the field — Argentieri has systematically advanced both the theoretical underpinnings and practical implementations of auditory perception for robotic systems. His contributions span a rich methodological spectrum. Early work on broadband extensions of the MUSIC algorithm (78 citations) and modal analysis-based beamforming established him as an innovator in array processing techniques tailored for real-world robotic environments. He has equally championed binaural approaches, exploring learning-based localization, multi-source separation via non-negative tensor factorization, and the fusion of auditory cues with vision — reflecting a commitment to human-inspired robot hearing. Beyond acoustic localization, Argentieri has made thought-provoking contributions to developmental robotics, investigating how naive agents can autonomously discover spatial structure through sensorimotor experience. This breadth — from signal processing rigor to cognitive modeling — distinguishes him as a versatile and intellectually ambitious researcher whose work continues to shape how robots perceive and interpret their acoustic environments.

Research Focus

Key Achievements

Top Papers

1. 1
   A survey on sound source localization in robotics: From binaural to array processing methods

   148 citations · 2015
2. 2
   Broadband variations of the MUSIC high-resolution method for Sound Source Localization in Robotics

   78 citations · 2007
3. 3
   A learning-based approach to robust binaural sound localization

   37 citations · 2013
4. 4
   Modal Analysis Based Beamforming for Nearfield or Farfield Speaker Localization in Robotics

   28 citations · 2006
5. 5
   A binaural sound source localization method using auditive cues and vision

   28 citations · 2012
6. 6
   Learning agent’s spatial configuration from sensorimotor invariants

   22 citations · 2015
7. 7
   Binaural Systems in Robotics

   22 citations · 2013
8. 8
   Binaural Localization of Multiple Sound Sources by Non-Negative Tensor Factorization

   19 citations · 2018
9. 9
   A non-linear approach to space dimension perception by a naive agent

   19 citations · 2012
10. 10
    Towards a systematic study of binaural cues

    14 citations · 2012

Key Collaborators

Contact & Links