A researcher from Queen Mary University of London has won a new fellowship from the Royal Academy of Engineering (RAEng) to develop versatile algorithms that can separate and interpret sounds, based on the way the human auditory system works.
Dr Emmanouil Benetos will join the Centre for Digital Music at QMUL’s School of Electronic Engineering and Computer Science in September to start his five year RAEng Research Fellowship.
As well as financial support, the fellowship provides outstanding researchers with mentoring to enable them to establish independent careers in research.
“I’m thrilled to have been awarded this fellowship from the RAEng and to be returning to QMUL, where I studied for my PhD, to continue my research on audio analysis,” said Dr Benetos.
Machine listening is the process of discriminating and extracting information from sound using specially designed software. These techniques have many applications, including bioacoustics, centred on the analysis of natural sounds, security and surveillance, where they can be used for crime detection, and music technology applications for automatic indexing of music collections.
Despite advances in processing power that allow large volumes of data to be analysed, it is still a challenge to develop a tool that works for any application and can separate different sounds and discriminate between useful sound and noise.Dr Benetos added: “The tools I am creating are based on machine learning technology, meaning algorithms that can automatically learn from data. Combined with computational models on how the human ear operates, I hope this research will make a difference on how people approach sound and improve quality of life in a variety of applications.”
Dr Benetos is one of seven researchers to receive this year’s Research Fellowships from the RAEng.
Professor Ric Parker, Director of Research and Technology, Rolls-Royce Group, and Chair of the Academy's Research and Secondments Committee, said: “As with every year, the applications we received for these positions were numerous and of the highest quality, which makes the selection process very difficult. The winning candidates were truly outstanding, with a clear vision for their future work and career. I wish them all the best and an exciting future in research."
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