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Detalhes

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Publicações

2017

Automatic musical key estimation with adaptive mode bias

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

Publicação
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

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

Publicação
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

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

Publicação
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.

Teses
supervisionadas

2018

Analysis and optimization of RHV maintenance circuits

Autor
Anthony Cleto Teixeira

Instituição
UP-FEUP

2018

Análise e otimização do fluxo de informação dos Ecocentros, no Município do Porto

Autor
Beatriz Mendonça Guerreiro

Instituição
UP-FEUP

2017

Musically-Informed Adaptive Audio Reverberation

Autor
João Paulo Caetano Pereira Carvalheira Neves

Instituição
UP-FEUP

2017

Computer Sound Transformations Guided by Perceptually Motivated Features

Autor
Nuno Figueiredo Pires

Instituição
UP-FEUP

2017

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

Autor
Alonso Torres-Matarrita

Instituição
UP-FEUP