Applications of Computational Intelligence in Computer Music Composition

Abstract

Engaging computers in composing musical pieces is a challenging and trending field of research. The musical tasks that can be performed or aided by computers' computational powers, are numerous. This paper is concerned with applications of computational intelligence in music composition. Its main objective is to survey various computational intelligence techniques for performing miscellaneous music composition tasks. To achieve this objective, we first define each music composition task, then we discuss the recent applications of each, and the techniques adopted in them. We also highlight the most suitable techniques for performing each task. Our study shows that the most suitable techniques for human composers imitative systems are case-based reasoning and artificial neural networks. It is also shown that Markov models are more suitable for predicting musical notes based on the given previous notes. Genetic algorithms excel in chord progressions generation. Deep neural networks are clever at capturing temporal information of a musical piece. The state-of-the-art generative adversarial networks produce music as close as possible to real compositions. At the end of this study, we shed the light on many future research directions in the field of computer music composition.

Research on the Application of Hybrid Density Network Combined with Gaussian Model in Computer Music Choreography

“…The problem of computerized music choreography has been studied by previous authors and the corresponding computerized automatic sound system; music choreography system has been proposed. The systems can be broadly divided into two categories: one is based on Shirati (2006) [1] represented by traditional people designed by engineers of music and movement features and feature matching algo-rithms to select the target music from the constructed movement database, and the movement database usually consists of motion capture data; the other is based on Alemi (2017) [2] based on machine learning algorithms, directly constructing music dance mapping models, and generally, the mapping relationship between music and movement features is obtained through model training, so that the dance In this paper, a new framework for automatic music choreography systems is investigated to address the shortcomings of previous work in order to make the generated dance movements both novel and coherent and to analyze and match them with the target music while ensuring that the choreography system has sufficient generalization capability. The framework mainly includes early dataset construction, model training, and training.…”

“…Completing this job not only takes time, but it also necessitates a high level of talent on the part of the animator, restricting the scope of virtual character dance animation [1]. As a result, a successful dance synthesis algorithm technique can be applied to a variety of sectors, such as music-assisted dance instruction, video game character movement generation, human behavior research, and virtual reality [2]. Based on the foregoing, this paper proposes a novel solution as follows: an automatic music choreography algorithm that uses dance data, a deep learning algorithm to train the training model, and a combination of filtering conditions to generate the training model automatically and intelligently to meet the expected dance movements and arrange the dance based on the matching of music and action clips.…”

Multi-Track Music Generation Based on the AC Algorithm and Global Value Return Network

“…It requires not only considering the melody generation for individual tracks but also coordinating and synchronizing across tracks. Although existing researches have achieved certain results in the field of single-track music generation, the field of multi-track music generation remains an urgent problem to be solved in terms of how to effectively coordinate the generation process of each track, and how to comprehensively consider the global music structure and the long-term value return in the composition [3][4]. Among the many attempts, nature-inspired algorithms have received particular attention due to their effectiveness in optimisation and search problems.…”

Difficulty Controlled Piano Music Generation using GAN Approach

“…Music generation using computers is a wide field that is extensively researched [12] [13]. Multiple methods have been used to address this problem [14], including rule-based [15], genetics algorithms [16], Markov Models [17], and recently, deep neural network approaches [13].…”