Jordi Pons - Audio Engineering Researcher
After five years at the Music Technology Group, I’m pretty excited to announce that I have accepted a full-time position as a researcher at Dolby Laboratories in Barcelona! I’m very happy to see how professional opportunities around machine learning keep growing in Barcelona. Our beloved city is becoming, more and more, an important AI hub in south Europe — what’s great, because it allows me to keep doing my job while being close to my friends and family!
Which is the outtake of Artificial Intelligence (AI)? This is a recurrent conversation topic among AI practitioners, specialized journalists, and brave politicians. Although some simple concepts are clearly conveyed to the general audience, there are some others that are not so widely known. In this
Since AI is impacting our lives through products available in the marketplace, the goal of this post is to analyze what’s up with AI systems when consumed via the free market. In other words, AI is developed and consumed in a market-driven fashion and I would like to better understand which are the consequences of that. Hence, I’ll be focusing on the economic side of AI to show that for encouraging the main AI actors to behave ethically we better (directly) act over the market.
In this series of posts I have written a couple of articles discussing the pros & cons of spectrogram-based VGG architectures, to think about which is the role of the computer vision deep learning architectures in the audio field. Now is time to discuss what’s up with waveform-based VGGs!
Me: VGGs suck because they are computationally inefficient, and because they are a naive adoption of a computer vision architecture.
Random person on Internet: Jordi, you might be wrong. People use VGGs a lot!
Me: VGGs suck because they are computationally inefficient and because they are a naive adoption of a computer vision architecture.
Random person on Internet: Jordi, you might be wrong. People use VGGs a lot!
