Project title: Acoustic Intelligence – towards self-supervised deep neural acoustic analysis
Project type: Research project
Funding: OPUS Program, National Science Centre (NCN)
Duration: 2024 – 2028
Project team members:
dr hab. inż. Konrad Kowalczyk, prof. AGH – Principal Investigator (PI)
Project goal
In the Acoustic Intelligence project, we direct our focus towards Self-Supervised Learning (SSL) for audio applications. Contrary to conventional learning, the supervision within the SSL is supposed to be induced from the unlabeled data itself, by capturing its structure. Besides leveraging the label-dependency problem, SSL enables to train systems on orders of magnitude more data. Thus, the models can learn to distinguish more subtle patterns, which increases their robustness, compared to fully supervised cases. This concept, so far mostly investigated in the fields of Computer Vision and Natural Language Processing, has potential to revolutionize research in the acoustic domain. We aim to expand current knowledge with research on formulation of novel cost functions, derivation of mathematical models along with corresponding selfsupervised training procedures, design and preparation of appropriate experimental evaluations.
The main goal of the Acoustic Intelligence project is to introduce novel Universal Audio Representation (UAR), Universal Acoustic Analysis (UAA), and Universal Constituent Audio Signal Enhancement (UCASE) to enable creation of stand-alone intelligent machines that can autonomously learn to improve their performance in a broad range of audio-related tasks, even in unseen acoustic test conditions. Specifically, we aim to establish a new state-of-the-art with self-supervised acoustic signal enhancement and acoustic scene analysis.