Dr Çağdaş Bilen gained his PhD from NYU Tandon School of Engineering and went on to do postdoctoral work at Strasbourg University, Technicolor and INRIA. He has also worked in other research labs such as AT&T (Bell) Labs and HP Labs before joining Audio Analytic in 2018.
He has a keen interest in a greater sense of hearing.
Dr Bilen has authored articles in highly respected international journals and conferences, and holds numerous patents on the topics of audio and multimedia signal representation, estimation and modelling. These include topics such as on audio inverse problems (audio inpainting, source separation and audio compression) using nonnegative matrix factorization and on fast image search algorithms with sparsity and deep learning.
“My role at Audio Analytic allows me the opportunity to apply my passion for signal processing and machine learning and to explore how a greater sense of hearing can re-shape the way that humans and machines interact.”
Çağdaş leads Audio Analytic’s respected research team in developing core technologies and tools that can further advance the field of machine listening. This cutting-edge work has led to a number of significant technical breakthroughs and patents, such as loss function frameworks, post-biasing technology, a powerful temporal decision engine, and an approach to model evaluation called Polyphonic Sound Detection Score (PSDS) which has been adopted as an industry standard metric by the DCASE Challenge.