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Publications

Publications by Marcelo Freitas Caetano

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.

2015

Automatic Generation of Chord Progressions with an Artificial Immune System

Authors
Navarro, M; Caetano, M; Bernardes, G; de Castro, LN; Manuel Corchado, JM;

Publication
EVOLUTIONARY AND BIOLOGICALLY INSPIRED MUSIC, SOUND, ART AND DESIGN (EVOMUSART 2015)

Abstract
Chord progressions are widely used in music. The automatic generation of chord progressions can be challenging because it depends on many factors, such as the musical context, personal preference, and aesthetic choices. In this work, we propose a penalty function that encodes musical rules to automatically generate chord progressions. Then we use an artificial immune system (AIS) to minimize the penalty function when proposing candidates for the next chord in a sequence. The AIS is capable of finding multiple optima in parallel, resulting in several different chords as appropriate candidates. We performed a listening test to evaluate the chords subjectively and validate the penalty function. We found that chords with a low penalty value were considered better candidates than chords with higher penalty values.

2014

Real-time manipulation of syncopation in audio loops

Authors
Cocharro, D; Sioros, G; Caetano, M; Davies, MEP;

Publication
Proceedings - 40th International Computer Music Conference, ICMC 2014 and 11th Sound and Music Computing Conference, SMC 2014 - Music Technology Meets Philosophy: From Digital Echos to Virtual Ethos

Abstract
In this work we present a system that estimates and manipulates rhythmic structures from audio loops in realtime to perform syncopation transformations. The core of our system is a technique for the manipulation of syncopation in symbolic representations of rhythm. In order to apply this technique to audio signals we must first segment the audio loop into musical events using onset detection. Then, we use the symbolic syncopation transformation method to determine how to modify the rhythmic structure in order to change the syncopation. Finally we present two alternative methods to reconstruct the audio loop, one based on time scaling and the other on resampling. Our system, Loopalooza, is implemented as a freely available MaxForLive device to allow musicians and DJs to manipulate syncopation in audio loops in realtime. Copyright:

2013

Musical Instrument Sound Morphing Guided by Perceptually Motivated Features

Authors
Caetano, M; Rodet, X;

Publication
IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING

Abstract
Sound morphing is a transformation that gradually blurs the distinction between the source and target sounds. For musical instrument sounds, the morph must operate across timbre dimensions to create the auditory illusion of hybridmusical instruments. The ultimate goal of sound morphing is to perform perceptually linear transitions, which requires an appropriate model to represent the sounds being morphed and an interpolation function to obtain intermediate sounds. Typically, morphing techniques directly interpolate the parameters of the sound model without considering the perceptual impact or evaluating the results. Perceptual evaluations are cumbersome and not always conclusive. In this work, we seek parameters of a sound model that favor linear variation of perceptually motivated temporal and spectral features used to guide the morph towards more perceptually linear results. The requirement of linear variation of feature values gives rise to objective evaluation criteria for sound morphing. We investigate several spectral envelope morphing techniques to determine which spectral representation renders the most linear transformation in the spectral shape feature domain. We found that interpolation of line spectral frequencies gives the most linear spectral envelope morphs. Analogously, we study temporal envelope morphing techniques and we concluded that interpolation of cepstral coefficients results in the most linear temporal envelope morph.

2014

Theoretical Framework of A Computational Model of Auditory Memory for Music Emotion Recognition

Authors
Caetano, MF; Wiering, F;

Publication
Proceedings of the 15th International Society for Music Information Retrieval Conference, ISMIR 2014, Taipei, Taiwan, October 27-31, 2014

Abstract

2013

The Role of Time in Music Emotion Recognition: Modeling Musical Emotions from Time-Varying Music Features

Authors
Caetano, M; Mouchtaris, A; Wiering, F;

Publication
FROM SOUNDS TO MUSIC AND EMOTIONS

Abstract
Music is widely perceived as expressive of emotion. However, there is no consensus on which factors in music contribute to the expression of emotions, making it difficult to find robust objective predictors for music emotion recognition (MER). Currently, MER systems use supervised learning to map non time-varying feature vectors into regions of an emotion space guided by human annotations. In this work, we argue that time is neglected in MER even though musical experience is intrinsically temporal. We advance that the temporal variation of music features rather than feature values should be used as predictors in MER because the temporal evolution of musical sounds lies at the core of the cognitive processes that regulate the emotional response to music. We criticize the traditional machine learning approach to MER, then we review recent proposals to exploit the temporal variation of music features to predict time-varying ratings of emotions over the course of the music. Finally, we discuss the representation of musical time as the flow of musical information rather than clock time. Musical time is experienced through auditory memory, so music emotion recognition should exploit cognitive properties of music listening such as repetitions and expectations.

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