Paper is out | Jordi Pons

ICASSP 2022: accepted papers

By Jordi Pons in Conferences, Paper is out January 29, 2022

These are the papers we will be presenting at ICASSP 2021! Infinite thanks to all my collaborators for the amazing work 🙂

On Loss Functions and Evaluation Metrics for Music Source Separation by Enric Gusó, Jordi Pons, Santiago Pascual, Joan Serrà [Zenodo, arXiv].

PixInWav: Residual Steganography for Hiding Pixels in Audio by Margarita Geleta, Cristina Punti, Kevin McGuinness, Jordi Pons, Cristian Canton, Xavier Giro-i-Nieto [arXiv].

Preprint: Multichannel-based learning for audio object extraction

By Jordi Pons in Paper is out March 1, 2021

How to extract audio objects with deep learning – without explicitly learning to extract those? In our ICASSP paper we propose multichannel-based learning, a technique closely related to self-supervised learning, differentiable digital signal processing, and universal sound separation.

arXiv: https://arxiv.org/pdf/2102.06142.pdf

Preprint: On permutation invariant training for speech source separation

By Jordi Pons in Paper is out February 4, 2021

Our ICASSP paper studying permutation ambiguity on speaker-independent source separation models is now accessible on arXiv:

https://arxiv.org/pdf/2102.04945

ICASSP 2021: accepted papers

By Jordi Pons in Conferences, Paper is out January 4, 2021

These are the papers we will be presenting at ICASSP 2021:

Xiaoyu Liu, Jordi Pons. On permutation invariant training for speech source separation. [arxiv]
Daniel Arteaga, Jordi Pons. Multichannel-based learning for audio object extraction. [arxiv]
Jordi Pons, Santiago Pascual, Giulio Cengarle, Joan Serrà. Upsampling artifacts in neural audio synthesis. [arXiv, code]
Christian J Steinmetz, Jordi Pons, Santiago Pascual, Joan Serrà. Automatic multitrack mixing with a differentiable mixing console of neural audio effects. [arXiv, demo]
Joan Serrà, Jordi Pons, Santiago Pascual. SESQA: semi-supervised learning for speech quality assessment. [arXiv]

Infinite thanks to all my collaborators for the amazing work 🙂

ICASSP article: TensorFlow models in Essentia

By Jordi Pons in Code, Deep learning, Paper is out March 31, 2020

Essentia is a reference open-source C++/Python library for audio and music analysis. In this work, we present a set of algorithms that employ TensorFlow in Essentia that allow predictions with pre-trained deep learning models — and some of those are based on musicnn!

Here a link to the paper, and to a nice post on how to use it!