224 citations · 673 across the 26 of their papers we have counts for
27 papers
Composers' Evaluations of an AI Music Tool: Insights for Human-Centred Design
Eleanor Row, György Fazekas
We present a study that explores the role of user-centred design in developing Generative AI (GenAI) tools for music composition. Through semi-structured interviews with profession…
Pianist Identification Using Convolutional Neural Networks
Jingjing Tang, Geraint Wiggins, Gyorgy Fazekas
This paper presents a comprehensive study of automatic performer identification in expressive piano performances using convolutional neural networks (CNNs) and expressive features.…
AI as mediator between composers, sound designers, and creative media producers
Sebastian Löbbers, Mathieu Barthet, György Fazekas
Musical professionals who produce material for non-musical stakeholders often face communication challenges in the early ideation stage. Expressing musical ideas can be difficult,…
Sinusoidal Frequency Estimation by Gradient Descent
Ben Hayes, Charalampos Saitis, György Fazekas
Sinusoidal parameter estimation is a fundamental task in applications from spectral analysis to time-series forecasting. Estimating the sinusoidal frequency parameter by gradient d…
Rigid-Body Sound Synthesis with Differentiable Modal Resonators
Rodrigo Diaz, Ben Hayes, Charalampos Saitis +2
Physical models of rigid bodies are used for sound synthesis in applications from virtual environments to music production. Traditional methods such as modal synthesis often rely o…
Contrastive Audio-Language Learning for Music
Ilaria Manco, Emmanouil Benetos, Elio Quinton +1
As one of the most intuitive interfaces known to humans, natural language has the potential to mediate many tasks that involve human-computer interaction, especially in application…
Seeing Sounds, Hearing Shapes: a gamified study to evaluate sound-sketches
Sebastian Löbbers, György Fazekas
Sound-shape associations, a subset of cross-modal associations between the auditory and visual domain, have been studied mainly in the context of matching a set of purposefully cra…
Learning music audio representations via weak language supervision
Ilaria Manco, Emmanouil Benetos, Elio Quinton +1
Audio representations for music information retrieval are typically learned via supervised learning in a task-specific fashion. Although effective at producing state-of-the-art res…
Performer Identification From Symbolic Representation of Music Using Statistical Models
Syed Rifat Mahmud Rafee, Gyorgy Fazekas, Geraint A. ~Wiggins
Music Performers have their own idiosyncratic way of interpreting a musical piece. A group of skilled performers playing the same piece of music would likely to inject their unique…
Neural Waveshaping Synthesis
Ben Hayes, Charalampos Saitis, György Fazekas
We present the Neural Waveshaping Unit (NEWT): a novel, lightweight, fully causal approach to neural audio synthesis which operates directly in the waveform domain, with an accompa…
DoReMi: First glance at a universal OMR dataset
Elona Shatri, György Fazekas
The main challenges of Optical Music Recognition (OMR) come from the nature of written music, its complexity and the difficulty of finding an appropriate data representation. This…
Sketching sounds: an exploratory study on sound-shape associations
Sebastian Löbbers, Mathieu Barthet, György Fazekas
Sound synthesiser controls typically correspond to technical parameters of signal processing algorithms rather than intuitive sound descriptors that relate to human perception of s…
A Modulation Front-End for Music Audio Tagging
Cyrus Vahidi, Charalampos Saitis, György Fazekas
Convolutional Neural Networks have been extensively explored in the task of automatic music tagging. The problem can be approached by using either engineered time-frequency feature…
MusCaps: Generating Captions for Music Audio
Ilaria Manco, Emmanouil Benetos, Elio Quinton +1
Content-based music information retrieval has seen rapid progress with the adoption of deep learning. Current approaches to high-level music description typically make use of class…
Transfer Learning for Piano Sustain-Pedal Detection
Beici Liang, György Fazekas, Mark Sandler
Detecting piano pedalling techniques in polyphonic music remains a challenging task in music information retrieval. While other piano-related tasks, such as pitch estimation and on…
Optical Music Recognition: State of the Art and Major Challenges
Elona Shatri, György Fazekas
Optical Music Recognition (OMR) is concerned with transcribing sheet music into a machine-readable format. The transcribed copy should allow musicians to compose, play and edit mus…
A Critical Look at the Applicability of Markov Logic Networks for Music Signal Analysis
Johan Pauwels, György Fazekas, Mark B. Sandler
In recent years, Markov logic networks (MLNs) have been proposed as a potentially useful paradigm for music signal analysis. Because all hidden Markov models can be reformulated as…
A Feature Learning Siamese Model for Intelligent Control of the Dynamic Range Compressor
Di Sheng, György Fazekas
In this paper, a siamese DNN model is proposed to learn the characteristics of the audio dynamic range compressor (DRC). This facilitates an intelligent control system that uses au…
A Tutorial on Deep Learning for Music Information Retrieval
Keunwoo Choi, György Fazekas, Kyunghyun Cho +1
Following their success in Computer Vision and other areas, deep learning techniques have recently become widely adopted in Music Information Retrieval (MIR) research. However, the…
The Effects of Noisy Labels on Deep Convolutional Neural Networks for Music Tagging
Keunwoo Choi, George Fazekas, Kyunghyun Cho +1
Deep neural networks (DNN) have been successfully applied to music classification including music tagging. However, there are several open questions regarding the training, evaluat…
Transfer learning for music classification and regression tasks
Keunwoo Choi, György Fazekas, Mark Sandler +1
In this paper, we present a transfer learning approach for music classification and regression tasks. We propose to use a pre-trained convnet feature, a concatenated feature vector…
Convolutional Recurrent Neural Networks for Music Classification
Keunwoo Choi, George Fazekas, Mark Sandler +1
We introduce a convolutional recurrent neural network (CRNN) for music tagging. CRNNs take advantage of convolutional neural networks (CNNs) for local feature extraction and recurr…
Explaining Deep Convolutional Neural Networks on Music Classification
Keunwoo Choi, George Fazekas, Mark Sandler
Deep convolutional neural networks (CNNs) have been actively adopted in the field of music information retrieval, e.g. genre classification, mood detection, and chord recognition.…
Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions
Keunwoo Choi, George Fazekas, Mark Sandler
We introduce a novel playlist generation algorithm that focuses on the quality of transitions using a recurrent neural network (RNN). The proposed model assumes that optimal transi…