no code implementations • 5 Nov 2020 • Jordi Bonada, Merlijn Blaauw
Our system is an encoder-decoder model with two encoders, linguistic and acoustic, and one (acoustic) decoder.
no code implementations • 17 Aug 2020 • Darius Petermann, Pritish Chandna, Helena Cuesta, Jordi Bonada, Emilia Gomez
However, most of the research has been focused on a typical case which consists in separating vocal, percussion and bass sources from a mixture, each of which has a distinct spectral structure.
1 code implementation • 12 Feb 2020 • Pritish Chandna, Merlijn Blaauw, Jordi Bonada, Emilia Gomez
We present a deep learning based methodology for extracting the singing voice signal from a musical mixture based on the underlying linguistic content.
no code implementations • 22 Oct 2019 • Merlijn Blaauw, Jordi Bonada
We propose a sequence-to-sequence singing synthesizer, which avoids the need for training data with pre-aligned phonetic and acoustic features.
2 code implementations • 26 Mar 2019 • Pritish Chandna, Merlijn Blaauw, Jordi Bonada, Emilia Gomez
We present a deep neural network based singing voice synthesizer, inspired by the Deep Convolutions Generative Adversarial Networks (DCGAN) architecture and optimized using the Wasserstein-GAN algorithm.
Sound Audio and Speech Processing
no code implementations • 19 Feb 2019 • Merlijn Blaauw, Jordi Bonada, Ryunosuke Daido
There are many use cases in singing synthesis where creating voices from small amounts of data is desirable.
no code implementations • 9 Jul 2018 • Emilia Gómez, Merlijn Blaauw, Jordi Bonada, Pritish Chandna, Helena Cuesta
This paper summarizes some recent advances on a set of tasks related to the processing of singing using state-of-the-art deep learning techniques.
2 code implementations • 12 Apr 2017 • Merlijn Blaauw, Jordi Bonada
We present a new model for singing synthesis based on a modified version of the WaveNet architecture.