École de Technologie Supérieure

Location: Montréal, Quebec, Canada

 

Ph.D./M.Sc. Project:

Development of machine learning algorithms for in-ear biosignal monitoring

General Information

Research  fields:     Development of machine learning algorithms for in-ear biosignal monitoring

Advisors:    Prof. Jérémie Voix <Jeremie.Voix@etsmtl.ca>

Location:      École de technologie supérieure, Montréal, Quebec, Canada

Starting date:     Winter 2022 Semester

 

1        Description

The goal of this project is to provide usable and robust biosignal captures in the occluded earcanal for various applications, ranging from health monitoring, hand-free/silent interfaces to cognitive biometry (resulting from emotion recognition capabilities). Currently, heartbeat and breathing rates were successfully extracted from in-ear recordings [1]. A corpus of in-ear recorded Wearer Induced Disturbances (WID) was also used to train a classifier that was  successfully used to detect a wide range of non-verbal audio events (clicking of teeth, clearing the throat, saliva noise, coughing, talking, etc.) [2]. The student will work on improving the real-time robust classification of in-ear biosignal using machine learning approaches, merging HRV measurement and non-verbal event classification, thereby enabling the multimodal characterization of emotions, cognitive performance, and cardiovascular health from in-ear biosignals and non-verbal events.

[1] Martin, A., and Voix, J., “In-Ear Audio Wearable: Measurement of Heart and Breathing Rates for Health and Safety Monitoring,” IEEE Trans. Biomed. Eng., vol. 65, no. 6, pp. 1256–1263, Jun. 2018, doi: 10.1109/TBME.2017.2720463.
[2] Bouserhal, R.E., Chabot, P., Sarria-Paja, M., Cardinal, P., and Voix, J., “Classification of Nonverbal Human Produced Audio Events: A Pilot Study,” in Interspeech 2018, Hyderabad, India, Sep. 2018, pp. 1512–1516. doi: 10.21437/Interspeech.2018-2299.

 

2        Supervision and Funding

Supervision will be provided by Prof. Jérémie Voix. Prof. Voix is an acoustics specialist and chairholder of CRITIAS. Financement via la Chaire de recherche industrielle ÉTS-EERS en technologies intra-auriculaires (www.critias.ca) ainsi que via des stages MITACS au sein de la compagnie EERS Global Technologies Inc. (www.eers.ca)

 

3        Location

École de technologie supérieure is located in Montréal, Québec, Canada. Often described as an appealing blend of North American and European culture, Montréal is a safe, multicultural city, nice to live in, with an affordable cost of living. Since its inception in 2016, Montréal has constantly ranked as Quacquerilli Symonds’ Best Student City in North America. Montréal is also recognized for its quality of life. Close to both peaceful rural beauty and exciting ski slopes, this dynamic city offers lively districts and many green spaces. Located in the heart of the city, the ÉTS campus is easily reached by bicycle or public transit.

Since its creation, ÉTS has pursued a mission that is deeply rooted in all its activities: To meet the needs of the industrial sector, which is in need of engineers who have not only a good theoretical background, but also practical knowledge. To fulfil this mission, ÉTS has a unique partnership with the business and industrial spheres that includes both small and large companies. It stands out from other universities in Quebec because of the applied training it offers students, as well as its research activities conducted by and for companies. Furthermore, this position is affiliated with the ETS-EERS Industrial Research Chair in In-Ear Technologies (CRITIAS) located at the Carrefour d’innovation INGO, which offers a unique and intimate relationship with the industrial partner EERS, located just across the hall.

 

4        Requirements

  • Good oral and written communication skills in french and/or english Une préférence sera accordée pour les candidat.e.s maîtrisant le français, langue officielle du Québec
  • Bachelor’s degree in Mechanical Engineering or other with courses in material science, industrial design, er- gonomics, and human
  • Proficiency in signal processing
  • Experience with machine learning is an asset
  • Interest in speech science

 

5        How to Apply

Interested candidates should send to Prof. Jérémie Voix <Jeremie.Voix@etsmtl.ca>, their CV, university transcripts, contact information of suitable references, and a short statement (max. 1 page) describing how their experience is relevant to successfully carrying out this project.

Ph.D./M.Sc. Project: Development of machine learning algorithms for in-ear biosignal monitoring

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