Creating Music With Fuzzy Logic

Cádiz, Rodrigo F.

doi:10.3389/frai.2020.00059

Cited by 6 publications

(3 citation statements)

References 45 publications

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“…Research in the field of robot musicianship has a rich history (Rowe, 2004) and it has experienced an increasing interest in recent times (Bretan and Weinberg, 2016). Currently there are robotic performers that can achieve very expressive performance levels, particularly with reinforcement learning approaches (Hantrakul et al, 2018) and machines that can compose music in real-time based on inference rules (Cádiz, 2020), or with direct interaction with its environment and people (Miranda and Tikhanoff, 2005). However, the question of creativity in robot musicianship remains elusive.…”

Section: Introductionmentioning

confidence: 99%

Creativity in Generative Musical Networks: Evidence From Two Case Studies

et al. 2021

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Deep learning, one of the fastest-growing branches of artificial intelligence, has become one of the most relevant research and development areas of the last years, especially since 2012, when a neural network surpassed the most advanced image classification techniques of the time. This spectacular development has not been alien to the world of the arts, as recent advances in generative networks have made possible the artificial creation of high-quality content such as images, movies or music. We believe that these novel generative models propose a great challenge to our current understanding of computational creativity. If a robot can now create music that an expert cannot distinguish from music composed by a human, or create novel musical entities that were not known at training time, or exhibit conceptual leaps, does it mean that the machine is then creative? We believe that the emergence of these generative models clearly signals that much more research needs to be done in this area. We would like to contribute to this debate with two case studies of our own: TimbreNet, a variational auto-encoder network trained to generate audio-based musical chords, and StyleGAN Pianorolls, a generative adversarial network capable of creating short musical excerpts, despite the fact that it was trained with images and not musical data. We discuss and assess these generative models in terms of their creativity and we show that they are in practice capable of learning musical concepts that are not obvious based on the training data, and we hypothesize that these deep models, based on our current understanding of creativity in robots and machines, can be considered, in fact, creative.

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Section: Introductionmentioning

confidence: 99%

Creativity in Generative Musical Networks: Evidence From Two Case Studies

et al. 2021

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“…La Generación de Contenido -Content Generation-se refiere a la creación automática de imágenes, vídeos o música por parte de algoritmos de IA. Esto puede incluir la generación de arte, efectos especiales, o incluso guiones de películas (Cádiz, 2020). La personalización de contenido -Content Personalization-utiliza algoritmos de IA para adaptar la experiencia de visualización a las preferencias de los espectadores.…”

Section: Conceptos Claves De La Ia En La Producción Audiovisualunclassified

Capítulo 6. Inteligencia Artificial y la producción audiovisual

Hinojosa-Becerra,

Marín-Gutiérrez,

Maldonado-Espinosa

2024

Espejo de Monografías

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Examina cómo la Inteligencia Artificial está revolucionando la producción audiovisual, desde la escritura de guiones hasta la edición de video, proporcionando herramientas que pueden predecir el éxito de contenidos y personalizar producciones para audiencias específicas. A pesar de estas ventajas, la IA también plantea desafíos en cuanto a derechos de autor y creatividad, con preocupaciones sobre la originalidad de los contenidos generados por IA.

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“…Rodrigo [13] presented the key principles and ideas of fuzzy logic and its applications, with a focus on the fuzzy approximation theorem and fuzzy inputs as latent spaces. Second, he provides an updated study of fuzzy logic's applicability in musical applications.…”

Section: Literature Surveymentioning

confidence: 99%

Enriching Song Recommendation Through Facial Expression Using Deep Learning

Deore¹

2023

ISI

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The music recommendation systems are highly linked with the emotional response of the user as the majority of the music is based on the mood of the listener. A large number of researches have been performed for the detection of emotion through the use of a variety of different techniques. These approaches have been helpful in achieving the emotion of the subject using various devices and other hardware which can be highly expensive with very low rates of accuracy. Whereas the detection of expression of the subject can be useful in determining the mood or the emotion with a considerable degree of accuracy. Therefore, to achieve the effective identification of emotion of an individual for effective music recommendation has been proposed in this research paper. The presented approach utilizes image normalization and Convolutional Neural Networks (CNN) which are trained on a dataset consisting of a number of different emotional responses. This trained model is then used to determine the mood of the individual and recommend music based on the detected mood. The experimental evaluation of the approach is performed to determine the accuracy of the emotion recognition which has resulted in highly accurate results. We achieved 62.88% testing accuracy with MSE and RMSE values of 8.5 and 2.9 respectively. The obtained results are promising and show that the fuzzy classification technique optimizes the outcomes.

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Creating Music With Fuzzy Logic

Cited by 6 publications

References 45 publications

Creativity in Generative Musical Networks: Evidence From Two Case Studies

Creativity in Generative Musical Networks: Evidence From Two Case Studies

Capítulo 6. Inteligencia Artificial y la producción audiovisual

Enriching Song Recommendation Through Facial Expression Using Deep Learning

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