Objective
Human sounds, both bodily sounds (e.g. heart sounds) or voice, have been used by doctors for centuries as diagnostic signals for health and disease progression. However, while stethoscopes and microphones are very affordable, diagnosing through sound is a challenging task for medical professionals and requires years of training. The clinical training is generally based on shadowing experienced clinicians and repeating auscultation: this has scalability limits and imposes a considerable burden onto the health system.
Machine learning for human sounds has beHuman sounds, being them bodily sounds (e.g. heart sounds) or voice, have been used by doctors for centuries as diagnostics signals for health and disease progression. However, while stethoscopes and microphones are very affordable, diagnosing through sound is a challenging task for medical professionals and requires years of training. The clinical training is generally based on shadowing experienced clinicians and repeating auscultation: this has scalability limits and imposes a considerable burden onto the health system.
Machine learning for human sounds has been explored by the research community and is showing promise. In project ERC EAR we have advanced the state of the art in this respect for cardiac and respiratory health tasks. We showed that models could complement the skills of clinicians when diagnosing and constructed powerful and pioneering open pretrained models able to generalize to unseen cardiac and respiratory tasks with very good performance and limited labelled data for those tasks.
In project AURORA, we will leverage the research results and develop a software framework that aids clinical training on respiratory and cardiac sounds and acts as a support tool for diagnostics. This will unlock the potential of human sound based diagnostics, by helping overcoming its inherent complexity and allow to leverage its inherent affordability and therefore contributing to democratization of health.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC-POC - HORIZON ERC Proof of Concept Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2025-POC
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
CB2 1TN CAMBRIDGE
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.