A survey of computer systems for expressive music performance

Kirke, Alexis; Miranda, Eduardo Reck

doi:10.1145/1592451.1592454

Cited by 57 publications

(34 citation statements)

References 61 publications

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“…The annual RenCon competition welcomes researchers to render expressive musical performances given a nominal score, reviewed in [14]. Yet, we find three major advantageous aspects of studying expression with embodied robot musicians over computer-generated music.…”

Section: Related Workmentioning

confidence: 99%

“…As stated in [14], "neurological and physical modeling of performance should go beyond [artificial neural networks] and instrument physical modeling. The human/instrument performance process is a complex dynamical system for which there have been some deeper psychological and physical studies.…”

Section: Aspect #3 a Platform For Developing Holistic Models For Emomentioning

confidence: 99%

“…Simple: Music researchers have found an abundant list of features to analyze and create emotional music. For instance, high tempo is correlated with happiness or anger, and low volume with sadness [14]. How can we most economically account for the differences in emotion?…”

Section: Requirementsmentioning

confidence: 99%

See 2 more Smart Citations

Towards expressive musical robots: a cross-modal framework for emotional gesture, voice and music

Lim

Ogata

Okuno

2012

J AUDIO SPEECH MUSIC PROC.

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It has been long speculated that expression of emotions from different modalities have the same underlying 'code', whether it be a dance step, musical phrase, or tone of voice. This is the first attempt to implement this theory across three modalities, inspired by the polyvalence and repeatability of robotics. We propose a unifying framework to generate emotions across voice, gesture, and music, by representing emotional states as a 4-parameter tuple of speed, intensity, regularity, and extent (SIRE). Our results show that a simple 4-tuple can capture four emotions recognizable at greater than chance across gesture and voice, and at least two emotions across all three modalities. An application for multi-modal, expressive music robots is discussed.

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Section: Related Workmentioning

confidence: 99%

Section: Aspect #3 a Platform For Developing Holistic Models For Emomentioning

confidence: 99%

See 1 more Smart Citation

Towards expressive musical robots: a cross-modal framework for emotional gesture, voice and music

Lim

Ogata

Okuno

2012

J AUDIO SPEECH MUSIC PROC.

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“…Surveys of expressive computer performance systems such as that carried out by (Kirke and Miranda, 2009) also provide a thorough overview of the extensive work carried out in the area of emotionally targeted computer aided music performance. Rowe (Rowe, 1992) describes three distinct approaches in generic algorithmic composition systems: generative, sequenced, or transformative.…”

Section: Affectively-driven Algorithmic Compositionmentioning

confidence: 99%

Affective Calibration of Musical Feature Sets in an Emotionally Intelligent Music Composition System

Williams

Kirke

Miranda

et al. 2017

ACM Trans. Appl. Percept.

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Affectively-driven algorithmic composition (AAC) is a rapidly growing field which exploits computer-aided composition in order to generate new music with particular emotional qualities or affective intentions. An AAC system was devised in order to generate a stimulus set covering 9 discrete sectors of a 2-dimensional emotion space by means of a 16 channel feed-forward artificial neural network. This system was used to generate a stimulus set of short pieces of music, which were rendered using a sampled piano timbre and evaluated by a group of experienced listeners who ascribed a two-dimensional valence-arousal co-ordinate to each stimulus. The underlying musical feature set, initially drawn from literature, was subsequently adjusted by amplifying or attenuating the quantity of each feature in order to maximize the spread of stimuli in the valence-arousal space before a second listener evaluation was conducted. This process was repeated a third time in order to maximize the spread of valence-arousal co-ordinates ascribed to the generated stimulus set in comparison to a spread taken from an existing pre-rated database of stimuli, demonstrating that this prototype AAC system is capable of creating short sequences of music with a slight improvement on the range of emotion found in a stimulus set comprised of real-world, traditionally composed musical excerpts.

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“…These deviations are even expected when we listen to a performance of a piece. If a performance is too well-timed it is often viewed as being robotic, lacking in expression and dynamics (Kirke & Miranda, 2009). …”

Section: Introductionmentioning

confidence: 99%

Generating Time: Rhythmic Perception, Prediction and Production with Recurrent Neural Networks

Elmsley

Weyde

Armstrong

2017

Journal of Creative Music Systems

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In the quest for a convincing musical agent that performs in real time alongside human performers, the issues surrounding expressively timed rhythm must be addressed. Current beat tracking methods are not sufficient to follow rhythms automatically when dealing with varying tempo and expressive timing. In the generation of rhythm, some existing interactive systems ignore the pulse entirely, or fix a tempo after some time spent listening to input. Since music unfolds in time, we take the view that musical timing needs to be at the core of a music generation system. Our research explores a connectionist machine learning approach to expressive rhythm generation, based on cognitive and neurological models. Two neural network models are combined within one integrated system. A Gradient Frequency Neural Network (GFNN) models the perception of periodicities by resonating nonlinearly with the musical input, creating a hierarchy of strong and weak oscillations that relate to the metrical structure. A Long Short-term Memory Recurrent Neural Network (LSTM) models longer-term temporal relations based on the GFNN output.The output of the system is a prediction of when in time the next rhythmic event is likely to occur. These predictions can be used to produce new rhythms, forming a generative model.We have trained the system on a dataset of expressively performed piano solos and evaluated its ability to accurately predict rhythmic events. Based on the encouraging results, we conclude that the GFNN-LSTM model has great potential to add the ability to follow and generate expressive rhythmic structures to real-time interactive systems.

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A survey of computer systems for expressive music performance

Cited by 57 publications

References 61 publications

Towards expressive musical robots: a cross-modal framework for emotional gesture, voice and music

Towards expressive musical robots: a cross-modal framework for emotional gesture, voice and music

Affective Calibration of Musical Feature Sets in an Emotionally Intelligent Music Composition System

Generating Time: Rhythmic Perception, Prediction and Production with Recurrent Neural Networks

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