DYNI : DYNamiques de l’Information

Keys Words

Artificial Intelligence, Deep Learning, Cognition, Bioacoustics, Speech, Hearing, Scene Understanding, Environmental Survey, Astrophysics

 

Head

Ricard MARXER

 

Permanent Members

GLOTIN HervéProfesseur des Universités
Courriel : herve.glotin@lis-lab.fr
Telephone : 0494142824
MARXER RicardMaitre de Conférences
Courriel : ricard.marxer@lis-lab.fr
PAIEMENT AdelineMaitre de Conférences
Courriel : adeline.paiement@lis-lab.fr
PARIS SébastienMaitre de Conférences
Courriel : sebastien.paris@lis-lab.fr
Telephone : 0494142647
RAZIK JosephMaitre de Conférences
Courriel : joseph.razik@lis-lab.fr
Telephone : 0494142826

 

Phd Studients

FERRARI MaxenceDoctorant
Courriel : maxence.ferrari@lis-lab.fr
MORGAN JayDoctorant
Courriel : jay.morgan@lis-lab.fr
PATRIS JulieDoctorant
Courriel : julie.patris@lis-lab.fr
POUPARD MarionDoctorant
Courriel : marion.poupard@lis-lab.fr

 

Other staff members

GIRAUDET PascaleCherch. associe
Courriel : pascale.giraudet@lis-lab.fr
MALIGE FranckPRAG
Courriel : franck.malige@lis-lab.fr
SCHLÜTER JanPost-Doctorant
Courriel : jan.schluter@lis-lab.fr

 

Research interests

Our aim is develop and innovate in methods of machine learning, signal processing and data analysis in order to improve our knowledge and understanding in physical, natural and human sciences.

DYNI’s research in Artificial Intelligence and representation learning aims to cover the data acquisition, transmission and processing chain from sensors to users. The research is applied to diverse fields such as, marine and maritime robotics, bioacoustics, speech & hearing, and multimodal information analysis in physics, health or cognition. Through its technological platform SMIoT, DYNI innovates the scientific instrumentation for smart long-term data acquisition and embedded data processing.

DYNI tackles the main challenges of data-driven approaches applied to the experimental sciences: a) high cost of data acquisition, b) human bias in manual annotations, c) complexity of the underlying phenomena, and d) explicatory power of advanced machine learning models. Thus, our work focuses on unsupervised/semi-supervised learning, reinforcement learning, Deep Learning and explainable AI applied to a variety of topics ranging from environmental monitoring to the study of vocal interactivity.

Prestigious researchers are invited to DYNI and its interaction with the economic, social and cultural environment is renowned. Its activity is very intense and diversified in the field of development (software, hardware, platform). It’s pioneering research activity has led to the creation of some of the first international congresses on environmental Big Data, the coordination of the Scaled Acoustic BIODiversity project (SABIOD), the EADM action of GDR MADICS, the organisation of ICLR 2017 and the co-organisation of the VIHAR community and workshops.



 

Site Web

More detail : https://dyni.lis-lab.fr

 

Scientific publications



6 documents

Article dans une revue

  • Kristina Yordanova, Stefan Lüdtke, Samuel Whitehouse, Frank Krüger, Adeline Paiement, et al.. Analysing Cooking Behaviour in Home Settings: Towards Health Monitoring. Sensors, MDPI, In press. ⟨hal-02003387⟩
  • Najwa Alghamdi, Steve Maddock, Ricard Marxer, Jon Barker, Guy Brown. A corpus of audio-visual Lombard speech with frontal and profile views. Journal of the Acoustical Society of America, Acoustical Society of America, 2018, 143 (6), pp.EL523-EL529. ⟨10.1121/1.5042758⟩. ⟨hal-01867824⟩
  • Ricard Marxer, Jon Barker, Najwa Alghamdi, Steve Maddock. The impact of the Lombard effect on audio and visual speech recognition systems. Speech Communication, Elsevier : North-Holland, 2018, 100, pp.58-68. ⟨10.1016/j.specom.2018.04.006⟩. ⟨hal-01779704⟩

Communication dans un congrès

  • Jay Morgan, Adeline Paiement, Monika Seisenberger, Jane Williams, Adam Wyner. A Chatbot Framework for the Children's Legal Centre. The 31st international conference on Legal Knowledge and Information Systems (JURIX), Dec 2018, Groningen, Netherlands. ⟨hal-01878545v2⟩
  • Mandar Gogate, Ahsan Adeel, Ricard Marxer, Jon Barker, Amir Hussain. DNN Driven Speaker Independent Audio-Visual Mask Estimation for Speech Separation. Interspeech 2018, Sep 2018, Hybderabad, India. pp.2723-2727, ⟨10.21437/Interspeech.2018-2516⟩. ⟨hal-01868604⟩
  • Randall Balestriero, Romain Cosentino, Hervé Glotin, Richard Baraniuk. Spline Filters For End-to-End Deep Learning. 35th International Conference on Machine Learning, Jul 2018, stockholm, Sweden. ⟨hal-01879266⟩