报告主题：Deep Learning for Audio Classification
报告人：Prof. Wenwu Wang
报告内容简介：Audio classification (e.g. audio scene analysis, audio event detection and audio tagging) have a variety of potential applications in security surveillance, intelligent sensing for smart homes and cities, multimedia search and retrieval, and healthcare. This research area is under rapid development recently, having attracted increasing interest from both academia and industrialists. In this talk, we will present some recent and new development for several challenges related to this topic, including data challenges (e.g. DCASE challenges), acoustic modelling, feature learning, dealing with weakly labelled data, and learning with noisy labels. We will show some latest results of our proposed algorithms, such as the attention neural network algorithms for learning with weakly labelled data, and their results on AudioSet – a large scale dataset provided by Google, as compared with several baseline methods. We will also use some sound demos to illustrate the potentials of our proposed algorithms.
Wenwu Wang is a Professor in Signal Processing and Machine Learning, and a Co-Director of the Machine Audition Lab within the Centre for Vision Speech and Signal Processing, University of Surrey, UK. He has been a Senior Area Editor (2019-) and Associate Editor (2014-2018) for IEEE Transactions on Signal Processing. He was a Publication Co-Chair for ICASSP 2019, Brighton, UK, and will serve as Tutorial Chair for ICASSP 2024, Seoul, South Korea. His current research interests include blind signal processing, sparse signal processing, audio-visual signal processing, machine learning and perception, artificial intelligence, machine audition (listening), and statistical anomaly detection. He has (co)-authored over 250 publications in these areas.
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