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About

I am a Senior Researcher in CTM where I coordinate the Sound and Music Computing research group. My main area of research is in the application of digital signal processing and machine learning techniques to music information retrieval (MIR). My primary research interest has been in the automatic extraction of rhythmic structure from music signals, however I have also undertaken research on evaluation methodologies, music therapy, sparse signal processing methods, object based coding of music and the analysis of groove.

My current activity, as an FCT Investigator, is on the emerging field of creative-MIR, where I am exploring techniques for the perception and measurement of music compatibility for automatic music remixing and recombination. I am also an Associate Editor for IEEE/ACM Transactions on Audio Speech and Language Processing.

Interest
Topics
Details

Details

003
Publications

2019

Temporal convolutional networks for musical audio beat tracking

Authors
Davies, MEP; Böck, S;

Publication
2019 27th European Signal Processing Conference (EUSIPCO)

Abstract

2019

Seed: Resynthesizing environmental sounds from examples

Authors
Bernardes, G; Aly, L; Davies, MEP;

Publication
SMC 2016 - 13th Sound and Music Computing Conference, Proceedings

Abstract
In this paper we present SEED, a generative system capable of arbitrarily extending recorded environmental sounds while preserving their inherent structure. The system architecture is grounded in concepts from concatenative sound synthesis and includes three top-level modules for segmentation, analysis, and generation. An input audio signal is first temporally segmented into a collection of audio segments, which are then reduced into a dictionary of audio classes by means of an agglomerative clustering algorithm. This representation, together with a concatenation cost between audio segment boundaries, is finally used to generate sequences of audio segments with arbitrarily long duration. The system output can be varied in the generation process by the simple and yet effective parametric control over the creation of the natural, temporally coherent, and varied audio renderings of environmental sounds. Copyright: © 2016 First author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

2019

The Harmonix Set: Beats, Downbeats, and Functional Segment Annotations of Western Popular Music

Authors
Nieto, O; McCallum, M; Davies, MEP; Robertson, A; Stark, AM; Egozy, E;

Publication
Proceedings of the 20th International Society for Music Information Retrieval Conference, ISMIR 2019, Delft, The Netherlands, November 4-8, 2019

Abstract

2019

Multi-Task Learning of Tempo and Beat: Learning One to Improve the Other

Authors
Böck, S; Davies, MEP; Knees, P;

Publication
Proceedings of the 20th International Society for Music Information Retrieval Conference, ISMIR 2019, Delft, The Netherlands, November 4-8, 2019

Abstract

2018

A Generative Model for the Characterization of Musical Rhythms

Authors
Sioros, G; Davies, MEP; Guedes, C;

Publication
Journal of New Music Research

Abstract

Supervised
thesis

2018

Affective Music Listening: Using facial expressions to drive music recommendation

Author
João Pedro Dias B. de Carvalho

Institution
UP-FEUP

2018

Content-Based Creative Manipulation of Music Signals

Author
António Humberto e Sá Pinto

Institution
UP-FEUP

2017

Content-Based Creative Manipulation of Music Signals

Author
António Humberto Sá Pinto

Institution
UP-FEUP

2017

Automatic transcription of vocalized percussion

Author
António Filipe Santana Ramires

Institution
UP-FEUP

2017

Interactive Manipulation of Musical Melody in Audio Recordings

Author
Miguel Miranda Guedes da Rocha e Silva

Institution
UP-FEUP