75 citations · 156 across the 22 of their papers we have counts for
27 papers
A Comparative Analysis Of Latent Regressor Losses For Singing Voice Conversion
Brendan O'Connor, Simon Dixon
Previous research has shown that established techniques for spoken voice conversion (VC) do not perform as well when applied to singing voice conversion (SVC). We propose an altern…
Interpreting Song Lyrics with an Audio-Informed Pre-trained Language Model
Yixiao Zhang, Junyan Jiang, Gus Xia +1
Lyric interpretations can help people understand songs and their lyrics quickly, and can also make it easier to manage, retrieve and discover songs efficiently from the growing mas…
Towards Robust Unsupervised Disentanglement of Sequential Data -- A Case Study Using Music Audio
Yin-Jyun Luo, Sebastian Ewert, Simon Dixon
Disentangled sequential autoencoders (DSAEs) represent a class of probabilistic graphical models that describes an observed sequence with dynamic latent variables and a static late…
A Convolutional-Attentional Neural Framework for Structure-Aware Performance-Score Synchronization
Ruchit Agrawal, Daniel Wolff, Simon Dixon
Performance-score synchronization is an integral task in signal processing, which entails generating an accurate mapping between an audio recording of a performance and the corresp…
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…
Learning Frame Similarity using Siamese networks for Audio-to-Score Alignment
Ruchit Agrawal, Simon Dixon
Audio-to-score alignment aims at generating an accurate mapping between a performance audio and the score of a given piece. Standard alignment methods are based on Dynamic Time War…
A Hybrid Approach to Audio-to-Score Alignment
Ruchit Agrawal, Simon Dixon
Audio-to-score alignment aims at generating an accurate mapping between a performance audio and the score of a given piece. Standard alignment methods are based on Dynamic Time War…
Automatic Lyrics Transcription using Dilated Convolutional Neural Networks with Self-Attention
Emir Demirel, Sven Ahlback, Simon Dixon
Speech recognition is a well developed research field so that the current state of the art systems are being used in many applications in the software industry, yet as by today, th…
Reliable Local Explanations for Machine Listening
Saumitra Mishra, Emmanouil Benetos, Bob L. Sturm +1
One way to analyse the behaviour of machine learning models is through local explanations that highlight input features that maximally influence model predictions. Sensitivity anal…
Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators
Daniel Stoller, Sebastian Ewert, Simon Dixon
Generative adversarial networks (GANs) have shown great success in applications such as image generation and inpainting. However, they typically require large datasets, which are o…
Investigating kernel shapes and skip connections for deep learning-based harmonic-percussive separation
Carlos Lordelo, Emmanouil Benetos, Simon Dixon +1
In this paper we propose an efficient deep learning encoder-decoder network for performing Harmonic-Percussive Source Separation (HPSS). It is shown that we are able to greatly red…
Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation
Daniel Stoller, Sebastian Ewert, Simon Dixon
Models for audio source separation usually operate on the magnitude spectrum, which ignores phase information and makes separation performance dependant on hyper-parameters for the…
Jointly Detecting and Separating Singing Voice: A Multi-Task Approach
Daniel Stoller, Sebastian Ewert, Simon Dixon
A main challenge in applying deep learning to music processing is the availability of training data. One potential solution is Multi-task Learning, in which the model also learns t…
Similarity measures for vocal-based drum sample retrieval using deep convolutional auto-encoders
Adib Mehrabi, Keunwoo Choi, Simon Dixon +1
The expressive nature of the voice provides a powerful medium for communicating sonic ideas, motivating recent research on methods for query by vocalisation. Meanwhile, deep learni…
Adversarial Semi-Supervised Audio Source Separation applied to Singing Voice Extraction
Daniel Stoller, Sebastian Ewert, Simon Dixon
The state of the art in music source separation employs neural networks trained in a supervised fashion on multi-track databases to estimate the sources from a given mixture. With…
Note Value Recognition for Piano Transcription Using Markov Random Fields
Eita Nakamura, Kazuyoshi Yoshii, Simon Dixon
This paper presents a statistical method for use in music transcription that can estimate score times of note onsets and offsets from polyphonic MIDI performance signals. Because p…
Robust Joint Alignment of Multiple Versions of a Piece of Music
Siying Wang, Sebastian Ewert, Simon Dixon
Large music content libraries often comprise multiple versions of a piece of music. To establish a link between different versions, automatic music alignment methods map each posit…
An End-to-End Neural Network for Polyphonic Piano Music Transcription
Siddharth Sigtia, Emmanouil Benetos, Simon Dixon
We present a supervised neural network model for polyphonic piano music transcription. The architecture of the proposed model is analogous to speech recognition systems and compris…