75 citations · 156 across the 22 of their papers we have counts for
7 papers · 1 filter
Zero-shot Singing Technique Conversion
Brendan O'Connor, Simon Dixon, George Fazekas
In this paper we propose modifications to the neural network framework, AutoVC for the task of singing technique conversion. This includes utilising a pretrained singing technique…
MSTRE-Net: Multistreaming Acoustic Modeling for Automatic Lyrics Transcription
Emir Demirel, Sven Ahlbäck, Simon Dixon
This paper makes several contributions to automatic lyrics transcription (ALT) research. Our main contribution is a novel variant of the Multistreaming Time-Delay Neural Network (M…
Pitch-Informed Instrument Assignment Using a Deep Convolutional Network with Multiple Kernel Shapes
Carlos Lordelo, Emmanouil Benetos, Simon Dixon +1
This paper proposes a deep convolutional neural network for performing note-level instrument assignment. Given a polyphonic multi-instrumental music signal along with its ground tr…
Computational Pronunciation Analysis in Sung Utterances
Emir Demirel, Sven Ahlback, Simon Dixon
Recent automatic lyrics transcription (ALT) approaches focus on building stronger acoustic models or in-domain language models, while the pronunciation aspect is seldom touched upo…
Low Resource Audio-to-Lyrics Alignment From Polyphonic Music Recordings
Emir Demirel, Sven Ahlbäck, Simon Dixon
Lyrics alignment in long music recordings can be memory exhaustive when performed in a single pass. In this study, we present a novel method that performs audio-to-lyrics alignment…
Structure-Aware Audio-to-Score Alignment using Progressively Dilated Convolutional Neural Networks
Ruchit Agrawal, Daniel Wolff, Simon Dixon
The identification of structural differences between a music performance and the score is a challenging yet integral step of audio-to-score alignment, an important subtask of music…
Adversarial Unsupervised Domain Adaptation for Harmonic-Percussive Source Separation
Carlos Lordelo, Emmanouil Benetos, Simon Dixon +2
This paper addresses the problem of domain adaptation for the task of music source separation. Using datasets from two different domains, we compare the performance of a deep learn…