Emotional Music Generation Using Interactive Genetic Algorithm

Zhu, Hua; Wang, Shangfei; Wang, Zhen

doi:10.1109/csse.2008.1203

Cited by 19 publications

(9 citation statements)

References 6 publications

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“…One of the most prominent reasons is that EAs do not require the use of gradient information to search the space, which in most scenarios cannot be computed for such problems. Examples of problems that capitalize on IEC algorithms include mental health measurement [16] and emotional music generation [17].…”

Section: Interactive Evolutionary Computationmentioning

confidence: 99%

Personalized Resource Allocation in Wireless Networks: An AI-Enabled and Big Data-Driven Multi-Objective Optimization

2020

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The design and optimization of wireless networks have mostly been based on strong mathematical and theoretical modeling. Nonetheless, as novel applications emerge in the era of 5G and beyond, unprecedented levels of complexity will be encountered in the design and optimization of the network. As a result, the use of Artificial Intelligence (AI) is envisioned for wireless network design and optimization due to the flexibility and adaptability it offers in solving extremely complex problems in real-time. One of the main future applications of AI is enabling user-level personalization for numerous use cases. AI will revolutionize the way we interact with computers in which computers will be able to sense commands and emotions from humans in a non-intrusive manner, making the entire process transparent to users. By leveraging this capability, and accelerated by the advances in computing technologies, wireless networks can be redesigned to enable the personalization of network services to the user level in real-time. While current wireless networks are being optimized to achieve a predefined set of quality requirements, the personalization technology advocated in this paper is supported by an intelligent big data-driven layer designed to micromanage the scarce network resources. This layer provides the intelligence required to decide the necessary service quality that achieves the target satisfaction level for each user. Due to its dynamic and flexible design, personalized networks are expected to achieve unprecedented improvements in optimizing two contradicting objectives in wireless networks: saving resources and improving user satisfaction levels. This article presents some foundational background on the proposed network personalization technology and its enablers. Then, an AI-enabled big data-driven surrogate-assisted multi-objective optimization formulation is proposed and tested to illustrate the feasibility and prominence of this technology.

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Section: Interactive Evolutionary Computationmentioning

confidence: 99%

Personalized Resource Allocation in Wireless Networks: An AI-Enabled and Big Data-Driven Multi-Objective Optimization

2020

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“…In this section, we assess our algorithm to generate emotional music [26] using a modified KTH rule system [27], whose parameters are optimized by an IGA. We compare music generated by our algorithm, a random algorithm, in which the parameters of …”

Section: Evaluation Experiments On Algorithms To Generate Emotion Musicmentioning

confidence: 99%

Evaluating emotional algorithms using psychological scales

Wang

2009

Proceedings of the International Workshop on Affective-Aware Virtual Agents and Social Robots

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Evaluating the effectiveness of emotional algorithms is a challenge in the research of affective computing. This paper presents an evaluation method using psychological scales. Affective interfaces and artifacts generated by different emotional algorithms are regarded as the external stimuli, while users' feelings are regarded as subjective evaluations. Psychophysical methods, such as the Scheffe method of paired comparison, are adopted to measure users' feelings and obtain a psychological scale. Thus objects generated by different algorithms can be compared on this psychological scale. The higher the value, the better the algorithm is. Empirical studies on three kinds of emotional algorithms, namely emotional image retrieval, emotional speech synthesis, and emotional music generation, are used to illustrate various uses of our approach and how it may be applied to evaluating affective interaction and user-centered design.

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“…The aid of human skills for that permits to model the musician's taste and even webbased human assessment of the individuals have been described in the literature (Putnam 1996;Tokui and Iba 2000;Fu et al 2006;Ayesh and Hugill 2005;Özcan and Erçal 2007;Zhu, Wang, and Wang 2008;Koga and Fukumoto 2014). Sometimes, the user is needed because the system is oriented to user interaction, like in the case of the GenJam system (Biles 1994), but sometimes the user is needed because the system is expected to accelerate the optimization by the user actions (Koga and Fukumoto 2014), like evaluating, removing, or even changing the melodies generated at a given generation.…”

Section: Human Supervised Fitness Functionsmentioning

confidence: 99%

“…Sometimes, the user is needed because the system is oriented to user interaction, like in the case of the GenJam system (Biles 1994), but sometimes the user is needed because the system is expected to accelerate the optimization by the user actions (Koga and Fukumoto 2014), like evaluating, removing, or even changing the melodies generated at a given generation. In (Zhu, Wang, and Wang 2008), the system is focused on capturing listener feelings, like happiness and sadness, although the user works in cooperation with a rule system to calculate the fitness values.…”

Section: Human Supervised Fitness Functionsmentioning

confidence: 99%

Data-based melody generation through multi-objective evolutionary computation

León

Iñesta

Calvo-Zaragoza

et al. 2016

Journal of Mathematics and Music

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Genetic-based composition algorithms are able to explore an immense space of possibilities, but the main difficulty has always been the implementation of the selection process. In this work, sets of melodies are utilized for training a machine learning approach to compute fitness, based on different metrics. The fitness of a candidate is provided by combining the metrics, but their values can range through different orders of magnitude and evolve in different ways, which makes it hard to combine these criteria. In order to solve this problem, a multi-objective fitness approach is proposed, in which the best individuals are those in the Pareto-optimal frontier of the multi-dimensional fitness space. Melodic trees are also proposed as a data structure for chromosomic representation of melodies and genetic operators are adapted to them. Some experiments have been carried out using a graphical interface prototype that allows one to explore the creative capabilities of the proposed system. An Online Supplement is provided where the reader can find some technical details, information about the data used, generated melodies, and additional information about the developed prototype and its performance.

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Emotional Music Generation Using Interactive Genetic Algorithm

Cited by 19 publications

References 6 publications

Personalized Resource Allocation in Wireless Networks: An AI-Enabled and Big Data-Driven Multi-Objective Optimization

Personalized Resource Allocation in Wireless Networks: An AI-Enabled and Big Data-Driven Multi-Objective Optimization

Evaluating emotional algorithms using psychological scales

Data-based melody generation through multi-objective evolutionary computation

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