Preprint: Upsampling layers for music source separation

We investigated various upsampling layers to consolidate the ideas we introduced in our previous paper. We benchmarked a large set of upsampling layers for music source separation: different transposed and subpixel convolution setups, different interpolation upsamplers (including two novel layers based on stretch and sinc interpolation), and different wavelet-based upsamplers (including a novel learnable wavelet layer).

Check our project website, and paper on arXiv!

WASPAA 2021 paper: “Adversarial auto-encoding for packet loss concealment”

PLAAE (packet loss adversarial auto-encoder) is our proposal for packet loss concealment in a non-autoregressive fashion. Our goal is to reconstruct missing speech packets until a new (real) packet is received in a video-call. Our end-to-end non-autoregressive adversarial auto-encoder specially shines at long-term predictions, beyond 60ms. The paper has been accepted for presentation at WASPAA 2021! Check out our arXiv pre-print.

Slides: Towards building an artistic discourse around music AI

On Thursday 13th May from 17:00 – 19:00 (CET) I’ll be part of the workshop ‘Exploring connections between AI and Music’. The live-streamed event is free to watch, and marks the presentation of the AI and Music Festival and its first activity (more information here). To prepare for it, I reviewed previous works by music AI artists and researchers. This slide deck contains a summary of how I perceive the current music AI scene.