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Hybrid and Interpretable Deep neural audio machines

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.

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2021-ADG

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Host institution

INSTITUT MINES-TELECOM
Net EU contribution

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.

€ 2 482 317,50
Address
19 PLACE MARGUERITE PEREY
91120 Palaiseau
France

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Region
Ile-de-France Ile-de-France Essonne
Activity type
Higher or Secondary Education Establishments
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Total cost

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.

€ 2 482 317,50

Beneficiaries (1)

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