On this episode of the Machine Listening podcast I was joined by two guests. Professor Mark Plumbley, Professor of Signal Processing at the UK’s University of Surrey, and Dr Cagdas Bilen, Head of Research at Audio Analytic, who joined me as a co-host.

In previous episodes we’ve talked about the acoustic spaces around us, the value of adding a sense of hearing to products as diverse as planetary robot explorers and the importance of realising that hearing isn’t just about the ears. This time we focus on the process of teaching machines to listen by talking to one of the pre-eminent academics in this field.

We talked about Mark’s interest in the field and his career before talking about the challenges that face researchers and how research needs to shift to starting with user needs.

When we touched on the DCASE Challenge, Mark said: “I’d like us to move away from the focus on ‘can we get the biggest numbers possible on some data measure’ but actually think about what the benefit is to people.”

He also called on the ML community to improve the visibility of the things that don’t work, so that researchers and students aren’t repeating the same mistakes.

“We are not working in the methodical way that say the medical research community might be where you need to register a study before you even try it. If it doesn’t work, everybody knows that it doesn’t work because you had to register it first. So you can’t sort of pretend that you didn’t try. Within the machine learning community as a whole we need to grapple with this and work out how do we improve the visibility of the things that don’t work, so it’s not just the tip of the iceberg that happen to work – maybe by chance – that you get to hear about.”

You can listen to the episode here, or wherever you get your podcasts:

To learn more about Mark’s work you can visit the University of Surrey website and follow Mark on Twitter. To learn more about the research on using sound recognition to detect depression click here, and to learn more about DCASE here.

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