Project description
Using AI to better understand sound
AI depends heavily on deep neural networks. However, these networks have two major limitations. First, they are very complex and need huge amounts of data to be trained and considerable computing power to be efficient. Second, they continue to be difficult to interpret. To tackle these deficiencies, the ERC-funded HI-Audio project aims to develop hybrid approaches that bring together signal processing and deep machine learning to understand and analyse sound. It will make use of innovative deterministic and statistical audio and sound environment models with dedicated neural autoencoders and generative networks. The project will also focus on particular applications, such as music and audio scene analysis.
Objective
Machine Listening, or AI for Sound, is defined as the general field of Artificial Intelligence applied to audio analysis, understanding and synthesis by a machine. The access to ever increasing super-computing facilities, combined with the availability of huge data repositories (although largely unannotated), has led to the emergence of a significant trend with pure data-driven machine learning approaches. The field has rapidly moved towards end-to-end neural approaches which aim to directly solve the machine learning problem for raw acoustic signals but often only loosely taking into account the nature and structure of the processed data. The main consequences are that the models are 1) overly complex, require massive amounts of data to be trained and extreme computing power to be efficient (in terms of task performance), and 2) remain largely unexplainable and non-interpretable. To overcome these major shortcomings, we believe that our prior knowledge about the nature of the processed data, their generation process and their perception by humans should be explicitly exploited in neural-based machine learning frameworks.
The aim of HI-Audio is to build such hybrid deep approaches combining parameter-efficient and interpretable signal models, musicological and physics-based models, with highly tailored, deep neural architectures. The research directions pursued in HI-Audio will exploit novel deterministic and statistical audio and sound environment models with dedicated neural auto-encoders and generative networks and target specific applications including speech and audio scene analysis, music information retrieval and sound transformation and synthesis.
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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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
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)
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Topic(s)
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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
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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 - HORIZON ERC Grants
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2021-ADG
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91120 Palaiseau
France
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