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About

Gilberto Bernardes is a Portuguese saxophonist and new media artist/researcher. He holds a Ph.D. in digital media from the University of Porto (Portugal) under the UT Austin-Portugal Program, a master in music performance from the Conservatory of Amsterdam, a Bachelors in music performance from the Superior School of Music and Performing Arts (Porto, Portugal), and a  "Premier Prix" in saxophone and chamber music from the ENM d'Issy-les-Moulineaux. Bernardes performs regurarly in Europe. Of notice his presence at the International Saxophone Week (Amsterdam, The Netherlands) with the premier of the saxophone transcription of "Dialogues de l'Ombre Double" by Pierre Boulez and the Gaudeamus Music Week at the Bimhuis (Amsterdam, The Netherlands). He also counts with several solo performances with orchestras, such as Gulbenkian Orchestra, Portuguese Symphonic Band, and Coruña Symphonic Band. 

Bernardes is a founding member of the Oporto Saxophone Quartet (sponsored by Yamaha), duo Blank Page, and Unisex Quartet, and a member of the contemporary music ensemble Oficina Musical. He is an Assistant Professor at Superior School of Music and Performing Arts (Polytechnic Institute of Porto) and pursues research as a post-doctoral researcher at INESC TEC (Porto, Portugal) on generative music.

Interest
Topics
Details

Details

Publications

2017

Automatic musical key estimation with adaptive mode bias

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

Publication
2017 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2017, New Orleans, LA, USA, March 5-9, 2017

Abstract
In this paper we present the INESC Key Detection (IKD) system which incorporates a novel method for dynamically biasing key mode estimation using the spatial displacement of beat-synchronous Tonal Interval Vectors (TIVs). We evaluate the performance of the IKD system at finding the global key on three annotated audio datasets and using three key-defining profiles. Results demonstrate the effectiveness of the mode bias in favoring either the major or minor mode, thus allowing users to fine tune this variable to improve correct key estimates on style-specific music datasets or to balance predictions across key modes on unknown input sources. © 2017 IEEE.

2016

A multi-level tonal interval space for modelling pitch relatedness and musical consonance

Authors
Bernardes, G; Cocharro, D; Caetano, M; Guedes, C; Davies, MEP;

Publication
JOURNAL OF NEW MUSIC RESEARCH

Abstract
In this paper we present a 12-dimensional tonal space in the context of the Tonnetz, Chew's Spiral Array, and Harte's 6-dimensional Tonal Centroid Space. The proposed Tonal Interval Space is calculated as the weighted Discrete Fourier Transform of normalized 12-element chroma vectors, which we represent as six circles covering the set of all possible pitch intervals in the chroma space. By weighting the contribution of each circle (and hence pitch interval) independently, we can create a space in which angular and Euclidean distances among pitches, chords, and regions concur with music theory principles. Furthermore, the Euclidean distance of pitch configurations from the centre of the space acts as an indicator of consonance.

2016

Harmony Generation Driven by a Perceptually Motivated Tonal Interval Space

Authors
Bernardes, G; Cocharro, D; Guedes, C; Davies, MEP;

Publication
COMPUTERS IN ENTERTAINMENT

Abstract
We present D'accord, a generative music system for creating harmonically compatible accompaniments of symbolic and musical audio inputs with any number of voices, instrumentation, and complexity. The main novelty of our approach centers on offering multiple ranked solutions between a database of pitch configurations and a given musical input based on tonal pitch relatedness and consonance indicators computed in a perceptually motivated Tonal Interval Space. Furthermore, we detail a method to estimate the key of symbolic and musical audio inputs based on attributes of the space, which underpins the generation of key-related pitch configurations. The system is controlled via an adaptive interface implemented for Ableton Live, MAX, and Pure Data, which facilitates music creation for users regardless of music expertise and simultaneously serves as a performance, entertainment, and learning tool. We perform a threefold evaluation of D'accord, which assesses the level of accuracy of our key-finding algorithm, the user enjoyment of generated harmonic accompaniments, and the usability and learnability of the system.

Supervised
thesis

2017

Compor para Aprender

Author
Isabela Corintha de Almeida

Institution
UP-FEUP

2017

Performative sound design

Author
Luís Alberto Teixeira Aly

Institution
UP-FEUP

2017

Moto-Var: towards new paths in interactive-assisted composition

Author
Alonso Torres-Matarrita

Institution
UP-FEUP

2017

Musically-Informed Adaptive Audio Reverberation

Author
João Paulo Caetano Pereira Carvalheira Neves

Institution
UP-FEUP

2017

Computer Sound Transformations Guided by Perceptually Motivated Features

Author
Nuno Figueiredo Pires

Institution
UP-FEUP