4 citations · 9 across the 4 of their papers we have counts for
9 papers
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…
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…
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…
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…
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…
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…