activity
20162025
most citedConvolutional Recurrent Neural Networks for Music Classification

58 citations · 67 across the 14 of their papers we have counts for

collaborators

14 papers

cs.SD2025

CoDiCodec: Unifying Continuous and Discrete Compressed Representations of Audio

Marco Pasini, Stefan Lattner, George Fazekas

Efficiently representing audio signals in a compressed latent space is critical for latent generative modelling. However, existing autoencoders often force a choice between continu…

cs.LG2025

Towards a Unified Representation Evaluation Framework Beyond Downstream Tasks

Christos Plachouras, Julien Guinot, George Fazekas +3

Downstream probing has been the dominant method for evaluating model representations, an important process given the increasing prominence of self-supervised learning and foundatio…

cs.SD2025

Towards An Integrated Approach for Expressive Piano Performance Synthesis from Music Scores

Jingjing Tang, Erica Cooper, Xin Wang +2

This paper presents an integrated system that transforms symbolic music scores into expressive piano performance audio. By combining a Transformer-based Expressive Performance Rend…

cs.LG2024

Continuous Autoregressive Models with Noise Augmentation Avoid Error Accumulation

Marco Pasini, Javier Nistal, Stefan Lattner +1

Autoregressive models are typically applied to sequences of discrete tokens, but recent research indicates that generating sequences of continuous embeddings in an autoregressive m…

cs.IR2024

Low-Data Classification of Historical Music Manuscripts: A Few-Shot Learning Approach

Elona Shatri, Daniel Raymond, George Fazekas

In this paper, we explore the intersection of technology and cultural preservation by developing a self-supervised learning framework for the classification of musical symbols in h…

cs.CV2024

Synthesising Handwritten Music with GANs: A Comprehensive Evaluation of CycleWGAN, ProGAN, and DCGAN

Elona Shatri, Kalikidhar Palavala, George Fazekas

The generation of handwritten music sheets is a crucial step toward enhancing Optical Music Recognition (OMR) systems, which rely on large and diverse datasets for optimal performa…