Why spectrogram-based VGGs suck? – Towards Data Science

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! No more introduction is required, this series of posts is about that: I want to share my honest thoughts regarding this discussion, for thinking which is the role of the computer vision deep learning architectures in the audio field. In these posts, I'm centering my discussion around the VGG model -- which is a computer vision architecture that is widely used by audio researchers.