ICASSP 2022 – my learnings

By Jordi Pons in Conferences, Deep learning

My biggest learning this year: I’LL NOT SURVIVE ANOTHER ONLINE CONFERENCE 💔 I really miss in-person discussion in exotic places! This year I attended ICASSP to present two papers:

  • “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].

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ICASSP 2022 paper: “On loss functions and evaluation metrics for music source separation”

By Jordi Pons in Conferences, Deep learning, Paper is out

During his internship at Dolby, Enric run an exhaustive evaluation of various loss functions for music source separation. After evaluating those losses objectively and subjectively, we recommend training with the following spectrogram-based losses: L2freq, SISDRfreq, LOGL2freq or LOGL1freq with, potentially, phase- sensitive objectives and adversarial regularizers.

link to arXiv!

ICASSP 2022: accepted papers

By Jordi Pons in Conferences, Paper is out

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: Upsampling layers for music source separation

By Jordi Pons in Conferences, Deep learning

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!