Search Results for author: Marco A. Martínez Ramírez

Found 7 papers, 4 papers with code

Distortion Audio Effects: Learning How to Recover the Clean Signal

no code implementations3 Feb 2022 Johannes Imort, Giorgio Fabbro, Marco A. Martínez Ramírez, Stefan Uhlich, Yuichiro Koyama, Yuki Mitsufuji

Given the recent advances in music source separation and automatic mixing, removing audio effects in music tracks is a meaningful step toward developing an automated remixing system.

Music Source Separation

Differentiable Signal Processing With Black-Box Audio Effects

2 code implementations11 May 2021 Marco A. Martínez Ramírez, Oliver Wang, Paris Smaragdis, Nicholas J. Bryan

We present a data-driven approach to automate audio signal processing by incorporating stateful third-party, audio effects as layers within a deep neural network.

Audio Signal Processing

AMSS-Net: Audio Manipulation on User-Specified Sources with Textual Queries

1 code implementation28 Apr 2021 Woosung Choi, Minseok Kim, Marco A. Martínez Ramírez, Jaehwa Chung, Soonyoung Jung

This paper proposes a neural network that performs audio transformations to user-specified sources (e. g., vocals) of a given audio track according to a given description while preserving other sources not mentioned in the description.

Modeling plate and spring reverberation using a DSP-informed deep neural network

1 code implementation22 Oct 2019 Marco A. Martínez Ramírez, Emmanouil Benetos, Joshua D. Reiss

Plate and spring reverberators are electromechanical systems first used and researched as means to substitute real room reverberation.

A general-purpose deep learning approach to model time-varying audio effects

no code implementations15 May 2019 Marco A. Martínez Ramírez, Emmanouil Benetos, Joshua D. Reiss

Audio processors whose parameters are modified periodically over time are often referred as time-varying or modulation based audio effects.

Ensemble Models for Spoofing Detection in Automatic Speaker Verification

1 code implementation9 Apr 2019 Bhusan Chettri, Daniel Stoller, Veronica Morfi, Marco A. Martínez Ramírez, Emmanouil Benetos, Bob L. Sturm

Our ensemble model outperforms all our single models and the baselines from the challenge for both attack types.

Audio and Speech Processing Sound

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