activity
20182022
most citedOn Filter Generalization for Music Bandwidth Extension Using Deep Neural Networks

27 citations · 36 across the 5 of their papers we have counts for

collaborators
Showing eess.ASShow all

5 papers · 1 filter

eess.AS2023

Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music

Ching-Yu Chiu, Meinard Müller, Matthew E. P. Davies +2

To model the periodicity of beats, state-of-the-art beat tracking systems use "post-processing trackers" (PPTs) that rely on several empirically determined global assumptions for t…

eess.AS20224 cited

An Analysis Method for Metric-Level Switching in Beat Tracking

Ching-Yu Chiu, Meinard Müller, Matthew E. P. Davies +2

For expressive music, the tempo may change over time, posing challenges to tracking the beats by an automatic model. The model may first tap to the correct tempo, but then may fail…

eess.AS20221 cited

Symbolic music generation conditioned on continuous-valued emotions

Serkan Sulun, Matthew E. P. Davies, Paula Viana

In this paper we present a new approach for the generation of multi-instrument symbolic music driven by musical emotion. The principal novelty of our approach centres on conditioni…

eess.AS202027 cited

On Filter Generalization for Music Bandwidth Extension Using Deep Neural Networks

Serkan Sulun, Matthew E. P. Davies

In this paper, we address a sub-topic of the broad domain of audio enhancement, namely musical audio bandwidth extension. We formulate the bandwidth extension problem using deep ne…

eess.AS20202 cited

TIV.lib: an open-source library for the tonal description of musical audio

António Ramires, Gilberto Bernardes, Matthew E. P. Davies +1

In this paper, we present TIV.lib, an open-source library for the content-based tonal description of musical audio signals. Its main novelty relies on the perceptually-inspired Ton…