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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

002
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.

2018

A Generative Model for the Characterization of Musical Rhythms

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

Publication
Journal of New Music Research

Abstract

2018

Preface

Authors
Aramaki, M; Davies, MEP; Kronland Martinet, R; Ystad, S;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract

2018

Music Technology with Swing - 13th International Symposium, CMMR 2017, Matosinhos, Portugal, September 25-28, 2017, Revised Selected Papers

Authors
Aramaki, M; Davies, MEP; Martinet, RK; Ystad, S;

Publication
CMMR

Abstract

Supervised
thesis

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

2017

Content-Based Creative Manipulation of Music Signals

Author
António Humberto Sá Pinto

Institution
UP-FEUP

2016

Cousax - Sistema de apoio e desenvolvimento a iniciantes de instrumento musical

Author
Cristiano Monteiro Jesus e Sousa

Institution
UP-FCUP

2016

201607908

Author
António Humberto Sá Pinto

Institution
UP-FEUP