102 citations · 231 across the 13 of their papers we have counts for
16 papers
DDSP-based Neural Waveform Synthesis of Polyphonic Guitar Performance from String-wise MIDI Input
Nicolas Jonason, Xin Wang, Erica Cooper +3
We explore the use of neural synthesis for acoustic guitar from string-wise MIDI input. We propose four different systems and compare them with both objective metrics and subjectiv…
Exploring Softly Masked Language Modelling for Controllable Symbolic Music Generation
Nicolas Jonason, Bob L. T. Sturm
This document presents some early explorations of applying Softly Masked Language Modelling (SMLM) to symbolic music generation. SMLM can be seen as a generalisation of masked lang…
Validity in Music Information Research Experiments
Bob L. T. Sturm, Arthur Flexer
Validity is the truth of an inference made from evidence, such as data collected in an experiment, and is central to working scientifically. Given the maturity of the domain of mus…
Audio Latent Space Cartography
Nicolas Jonason, Bob L. T. Sturm
We explore the generation of visualisations of audio latent spaces using an audio-to-image generation pipeline. We believe this can help with the interpretability of audio latent s…
TimbreCLIP: Connecting Timbre to Text and Images
Nicolas Jonason, Bob L. T. Sturm
We present work in progress on TimbreCLIP, an audio-text cross modal embedding trained on single instrument notes. We evaluate the models with a cross-modal retrieval task on synth…
The Beyond the Fence Musical and Computer Says Show Documentary
Simon Colton, Maria Teresa Llano, Rose Hepworth +11
During 2015 and early 2016, the cultural application of Computational Creativity research and practice took a big leap forward, with a project where multiple computational systems…
Dataset artefacts in anti-spoofing systems: a case study on the ASVspoof 2017 benchmark
Bhusan Chettri, Emmanouil Benetos, Bob L. T. Sturm
The Automatic Speaker Verification Spoofing and Countermeasures Challenges motivate research in protecting speech biometric systems against a variety of different access attacks. T…
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…
Ensemble Models for Spoofing Detection in Automatic Speaker Verification
Bhusan Chettri, Daniel Stoller, Veronica Morfi +3
Detecting spoofing attempts of automatic speaker verification (ASV) systems is challenging, especially when using only one modeling approach. For robustness, we use both deep neura…
A Study On Convolutional Neural Network Based End-To-End Replay Anti-Spoofing
Bhusan Chettri, Saumitra Mishra, Bob L. Sturm +1
The second Automatic Speaker Verification Spoofing and Countermeasures challenge (ASVspoof 2017) focused on "replay attack" detection. The best deep-learning systems to compete in…
The "Horse'' Inside: Seeking Causes Behind the Behaviours of Music Content Analysis Systems
Bob L. Sturm
Building systems that possess the sensitivity and intelligence to identify and describe high-level attributes in music audio signals continues to be an elusive goal, but one that s…
Music transcription modelling and composition using deep learning
Bob L. Sturm, João Felipe Santos, Oded Ben-Tal +1
We apply deep learning methods, specifically long short-term memory (LSTM) networks, to music transcription modelling and composition. We build and train LSTM networks using approx…
Deep Learning and Music Adversaries
Corey Kereliuk, Bob L. Sturm, Jan Larsen
An adversary is essentially an algorithm intent on making a classification system perform in some particular way given an input, e.g., increase the probability of a false negative.…
On Evaluation Validity in Music Autotagging
Fabien Gouyon, Bob L. Sturm, Joao Lobato Oliveira +2
Music autotagging, an established problem in Music Information Retrieval, aims to alleviate the human cost required to manually annotate collections of recorded music with textual…
The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use
Bob L. Sturm
The GTZAN dataset appears in at least 100 published works, and is the most-used public dataset for evaluation in machine listening research for music genre recognition (MGR). Our r…
Sparse Vector Distributions and Recovery from Compressed Sensing
Bob L. Sturm
It is well known that the performance of sparse vector recovery algorithms from compressive measurements can depend on the distribution underlying the non-zero elements of a sparse…