Spieldose: An Interactive Genetic Software for Assisting to Music Composition Tasks

Sánchez, Ángel Mediavilla; Pantrigo, Juan José; Virseda, Jesus; Pérez, Gabriela Cobo del Rosal

doi:10.1007/978-3-540-73053-8_62

Cited by 3 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sánchez et al [20] developed a tool called "Spieldose" or the music box, using interactive Genetic algorithm. The initial population of the algorithm parents is selected by the users.…”

Section: Related Workmentioning

confidence: 99%

Automatic Music Composition Using Genetic Algorithm and Artificial Neural Networks

Doush

Sawalha

2020

MJCS

View full text Add to dashboard Cite

The aim of this paper is to automatically compose new pleasing music from randomly generated notes without human intervention. To achieve this goal, Genetic Algorithm was implemented to generate random notes. The Neural Network was trained on a set of melodies to learn their regularity of patterns and then it is used as a fitness evaluator for the generated music from the Genetic Algorithm. Four Genetic Algorithms (using different combinations of tournament, roulette-wheel selections and one-point, two-point crossovers) were used in generating music to compare them according to which one is the most suitable for music composition. The experiments show that using tournament selection and two-point crossover produces better music patterns than using other combinations by 57%. The experiments show that the generated music was good and the results were promising. For evaluation, 10 music experts were asked to listen and evaluate four samples of the generated music; two of them were evaluated high from the Neural Network and two were evaluated low. Then we compared their results with the results from the Neural Network. The results show that the error rate for Neural Network was 16.7% and accuracy was 83.3%.

show abstract

“…Sánchez et al [20] developed a tool called "Spieldose" or the music box, using interactive Genetic algorithm. The initial population of the algorithm parents is selected by the users.…”

Section: Related Workmentioning

confidence: 99%

Automatic Music Composition Using Genetic Algorithm and Artificial Neural Networks

Doush

Sawalha

2020

MJCS

View full text Add to dashboard Cite

show abstract

“…Composers and engineers experimented with machine learning algorithms too, especially genetic algorithms (GAs), to try to simulate human works. For example, the Spieldose (Sanchez et al 2007) system used a GA with an interactive fitness function to make melody and harmony, and the Melodycomposition (Craane 2009) system used a GA with a programmatic fitness function to create solo melodies. GAs, however, have limitations for music.…”

Section: Related Workmentioning

confidence: 99%

CrowdBand: An Automated Crowdsourcing Sound Composition System

Pietrowicz

Chopra

Sadeghi

et al. 2013

HCOMP

View full text Add to dashboard Cite

CrowdBand, a sound composition system, demonstrates how a crowd can create works that meet requested criteria and fulfill the aesthetic character given by keyword description and examples. CrowdBand allows flexibility in music composition in terms of duration of the music, completion time and cost of music composition by giving the requestor two modes - thrifty and normal. CrowdBand’s workflow divides the composition task into three sections: requesting fundamental sounds, assembling sounds into compositions, and evaluating the results. Based on the crowd workers’ responses, we conclude that crowdsourced workers who are non-musicians can design sound and create novel sound compositions through CrowdBand. We also conclude that CrowdBand gives the musically-untrained crowd workers the ability to use common compositional techniques, such as sound layering, vertical stacking of sounds to create harmonic effects, related melodic lines (contrapuntal techniques), and transitions between aesthetic notions, or sound themes. Finally, we show improved, faster results with successive simplification and examples.

show abstract

Interactive music composition driven by feature evolution

Καλιακάτσος-Παπακώστας

Floros

Vrahatis

2016

SpringerPlus

View full text Add to dashboard Cite

Evolutionary music composition is a prominent technique for automatic music generation. The immense adaptation potential of evolutionary algorithms has allowed the realisation of systems that automatically produce music through feature and interactive-based composition approaches. Feature-based composition employs qualitatively descriptive music features as fitness landmarks. Interactive composition systems on the other hand, derive fitness directly from human ratings and/or selection. The paper at hand introduces a methodological framework that combines the merits of both evolutionary composition methodologies. To this end, a system is presented that is organised in two levels: the higher level of interaction and the lower level of composition. The higher level incorporates the particle swarm optimisation algorithm, along with a proposed variant and evolves musical features according to user ratings. The lower level realizes feature-based music composition with a genetic algorithm, according to the top level features. The aim of this work is not to validate the efficiency of the currently utilised setup in each level, but to examine the convergence behaviour of such a two-level technique in an objective manner. Therefore, an additional novelty in this work concerns the utilisation of artificial raters that guide the system through the space of musical features, allowing the exploration of its convergence characteristics: does the system converge to optimal melodies, is this convergence fast enough for potential human listeners and is the trajectory to convergence “interesting’ and “creative” enough? The experimental results reveal that the proposed methodological framework represents a fruitful and robust, novel approach to interactive music composition.

show abstract

Spieldose: An Interactive Genetic Software for Assisting to Music Composition Tasks

Cited by 3 publications

References 15 publications

Automatic Music Composition Using Genetic Algorithm and Artificial Neural Networks

Automatic Music Composition Using Genetic Algorithm and Artificial Neural Networks

CrowdBand: An Automated Crowdsourcing Sound Composition System

Interactive music composition driven by feature evolution

Contact Info

Product

Resources

About