As an attendee at Sónar+D 2023, I witnessed the cutting-edge advancements and trends in AI art. This renowned event brought together artists, technologists, and enthusiasts who collectively explored the intersection of artificial intelligence and creativity. From cool installations to thought-provoking discussions, Sónar 2023 provided a platform to delve into AI art world. In this blog post, I’ll cover the key trends and insights I observed at Sónar+D 2023!
It’s been amazing to re-meet my international friends and colleagues in person. It was nice to see PhD students to experience research and conferences firsthand (no beers allowed) 🙂 I’m sure this meeting will foster future collaborations and new friendships, pushing the field of music/audio deep learning research forward!
This year I was there to present:
- Full-band General Audio Synthesis with Score-based Diffusion by Santi Pascual, Gautam Bhattacharya, Chunghsin Yeh, Jordi Pons and Joan Serrà.
- Adversarial Permutation Invariant Training for Universal Sound Separation by Emilian Postolache, Jordi Pons, Santi Pascual and Joan Serrà.
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.Continue reading
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!
- Post I: Why do spectrogram-based VGGs suck?
- Post II: Why do spectrogram-based VGGs rock?
- Post III: What’s up with waveform-based VGGs? [this post]
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!Continue reading