activity
20182022
most citedWGANSing: A Multi-Voice Singing Voice Synthesizer Based on the Wasserstein-GAN

55 citations · 111 across the 8 of their papers we have counts for

collaborators

10 papers

cs.SD2022

Voice conversion with limited data and limitless data augmentations

Olga Slizovskaia, Jordi Janer, Pritish Chandna +1

Applying changes to an input speech signal to change the perceived speaker of speech to a target while maintaining the content of the input is a challenging but interesting task kn…

cs.SD2021

LoopNet: Musical Loop Synthesis Conditioned On Intuitive Musical Parameters

Pritish Chandna, António Ramires, Xavier Serra +1

Loops, seamlessly repeatable musical segments, are a cornerstone of modern music production. Contemporary artists often mix and match various sampled or pre-recorded loops based on…

eess.AS2020★ 2 cited

A Deep Learning Based Analysis-Synthesis Framework For Unison Singing

Pritish Chandna, Helena Cuesta, Emilia Gómez

Unison singing is the name given to an ensemble of singers simultaneously singing the same melody and lyrics. While each individual singer in a unison sings the same principle melo…

eess.AS2020★ 3 cited

Deep Learning Based Source Separation Applied To Choir Ensembles

Darius Petermann, Pritish Chandna, Helena Cuesta +2

Choral singing is a widely practiced form of ensemble singing wherein a group of people sing simultaneously in polyphonic harmony. The most commonly practiced setting for choir ens…

eess.AS2020

Content Based Singing Voice Extraction From a Musical Mixture

Pritish Chandna, Merlijn Blaauw, Jordi Bonada +1

We present a deep learning based methodology for extracting the singing voice signal from a musical mixture based on the underlying linguistic content. Our model follows an encoder…

eess.AS2019

Neural Percussive Synthesis Parameterised by High-Level Timbral Features

António Ramires, Pritish Chandna, Xavier Favory +2

We present a deep neural network-based methodology for synthesising percussive sounds with control over high-level timbral characteristics of the sounds. This approach allows for i…

cs.SD2019★ 12 cited

A Vocoder Based Method For Singing Voice Extraction

Pritish Chandna, Merlijn Blaauw, Jordi Bonada +1

This paper presents a novel method for extracting the vocal track from a musical mixture. The musical mixture consists of a singing voice and a backing track which may comprise of…

cs.SD2019★ 4 cited

A Framework for Multi-f0 Modeling in SATB Choir Recordings

Helena Cuesta, Emilia Gómez, Pritish Chandna

Fundamental frequency (f0) modeling is an important but relatively unexplored aspect of choir singing. Performance evaluation as well as auditory analysis of singing, whether indiv…

cs.SD2019★ 55 cited

WGANSing: A Multi-Voice Singing Voice Synthesizer Based on the Wasserstein-GAN

Pritish Chandna, Merlijn Blaauw, Jordi Bonada +1

We present a deep neural network based singing voice synthesizer, inspired by the Deep Convolutions Generative Adversarial Networks (DCGAN) architecture and optimized using the Was…

cs.SD2018★ 35 cited

Deep Learning for Singing Processing: Achievements, Challenges and Impact on Singers and Listeners

Emilia Gómez, Merlijn Blaauw, Jordi Bonada +2

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. We discuss their achievements in…