Algorithmic Music Composition Research Articles

NeuralPMG: A Neural Polyphonic Music Generation System Based on Machine Learning Algorithms

The realm of music composition, augmented by technological advancements such as computers and related equipment, has undergone significant evolution since the 1970s. In the field algorithmic composition, however, the incorporation of artificial intelligence (AI) in sound generation and combination has been limited. Existing approaches predominantly emphasize sound synthesis techniques, with no music composition systems currently employing Nicolas Slonimsky’s theoretical framework. This article introduce NeuralPMG, a computer-assisted polyphonic music generation framework based on a Leap Motion (LM) device, machine learning (ML) algorithms, and brain-computer interface (BCI). ML algorithms are employed to classify user’s mental states into two categories: focused and relaxed. Interaction with the LM device allows users to define a melodic pattern, which is elaborated in conjunction with the user’s mental state as detected by the BCI to generate polyphonic music. NeuralPMG was evaluated through a user study that involved 19 students of Electronic Music Laboratory at a music conservatory, all of whom are active in the music composition field. The study encompassed a comprehensive analysis of participant interaction with NeuralPMG. The compositions they created during the study were also evaluated by two domain experts who addressed their aesthetics, innovativeness, elaboration level, practical applicability, and emotional impact. The findings indicate that NeuralPMG represents a promising tool, offering a simplified and expedited approach to music composition, and thus represents a valuable contribution to the field of algorithmic music composition.

Algorithmic Composition of Music Utilizing the Digits of Pi

As a matter of fact, algorithmic composition, the fusion of computational methods with music creation, has a rich history dating back to Pythagoras. On this basis, this paper explores the innovative intersection of mathematics, algorithms, and music, focusing on the translation of Pi's digits into a musical composition. Through a combination of manual and automated techniques, the research successfully crafts a melody using the minor pentatonic scale and traditional Chinese instruments, enriched by an algorithmically generated counter-melody. According to the analysis, the study reveals both the potential and limitations of this approach, with the fixed nature of the accompaniment and strict adherence to the pentatonic scale constraining musical diversity. Nevertheless, the research offers a novel perspective on mathematical representation in music, contributing to the broader field of algorithmic music composition. The findings open new avenues for personalized music creation, educational tools, and artistic exploration, highlighting the limitless possibilities when art and science converge.

Exploring the Potential of Blockchain Data as Pseudorandom Numbers for Microtonal Algorithmic Composition. A Case Study on Blockchain Music 2.5

This study explores the utilization of blockchain data as a set of pseudorandom numbers in the context of microtonal algorithmic composition. Conventional methods of generating indiscriminate numbers often lack the desired levels of unpredictability and uniqueness necessary for an intricate musical piece. By harnessing the hash parameter of several blockchains, we propose an innovative approach for obtaining pseudorandom numbers that offer heightened randomness and diversity. Moreover, the ensuing structure ensures the preservation of all composition data on servers, facilitating the re-generation of the piece and enabling listeners to revisit its past fragments. Additionally, leveraging blockchain technology allows the audience to actively interact with the composition, fostering a more engaging and participatory musical experience. Through a comprehensive case study of a self-written, self-generating piece (Blockchain Music 2.5, 2023), we demonstrate the practical implementation of blockchain-generated numbers, showcasing their potential in exploring new post-tonal dimensions and producing unique sonic textures based on the predetermined set of rules. We also discuss the implications, challenges, and potential future directions of incorporating blockchain-generated numbers in algorithmic music composition. The research expands the landscape of digital music composition techniques and offers new avenues for leveraging blockchain technology in the domain of creative expression.

Unleashing Creative Synergies: A Mixed-Method Case Study in Music Education Classrooms

Algorithmic music composition has been gaining prominence and recognition as an innovative approach to music education, providing students with opportunities to explore creativity, computational thinking, and musical knowledge. This study aims to investigate the impact of integrating algorithmic music composition in the classroom, examining its influence on student engagement, musical knowledge, and creative expression, as well as to enhance computational thinking skills. A mixed-method case study was conducted in three Basic Music Education classrooms in the north of Portugal, involving 71 participants (68 students and 3 music teachers). The results reveal: (i) both successes and challenges in integrating computational thinking concepts and practices; (ii) pedagogical benefits of integrating programming platforms, where programming concepts overlapped with music learning outcomes; and (iii) positive impact on participants’ programming self-confidence and recognition of programming’s importance. Integrating algorithmic music composition in the classroom positively influences student engagement, musical knowledge, and creative expression. The use of algorithmic techniques provides a novel and engaging platform for students to explore music composition, fostering their creativity, critical thinking, and collaboration skills. Educators can leverage algorithmic music composition as an effective pedagogical approach to enhance music education, allowing students to develop a deeper understanding of music theory and fostering their artistic expression. Future research should contribute to the successful integration of digital technologies in the Portuguese curriculum by further exploring the long-term effects and potential applications of algorithmic music composition in different educational contexts.

Algorithmic music generation by harmony recombination with genetic algorithm

Algorithmic music composition has recently become an area of prestigious research in projects such as Google’s Magenta, Aiva, and Sony’s CSL Lab aiming to increase the composers’ tools for creativity. There are advances in systems for music feature extraction and generation of harmonies with short-time and long-time patterns of music style, genre, and motif. However, there are still challenges in the creation of poly-instrumental and polyphonic music, pieces become repetitive and sometimes these systems copy the original files. The main contribution of this paper is related to the improvement of generating new non-plagiary harmonic developments constructed from the symbolic abstraction from MIDI music non-labeled data with controlled selection of rhythmic features based on evolutionary techniques. Particularly, a novel approach for generating new music compositions by replacing existing harmony descriptors in a MIDI file with new harmonic features from another MIDI file selected by a genetic algorithm. This allows combining newly created harmony with a rhythm of another composition guaranteeing the adjustment of a new music piece to a distinctive genre with regularity and consistency. The performance of the proposed approach has been assessed using artificial intelligent computational tests, which assure goodness of the extracted features and shows its quality and competitiveness.

Interdisciplinary computational thinking with music and programming: a case study on algorithmic music composition with Sonic Pi

ABSTRACT Background and Context: Computational Thinking (CT) has been recently integrated into new and revised Digital Technologies content (DTC) in the Technology learning area of the New Zealand School Curriculum. Objective: To aid this change, this research examined how CT supports learning outcomes in both music and programming with the Sonic Pi platform. Method: A mixed-methods case study designed by the researcher was developed and trialled in a regular middle school classroom. The design of instruments and analysis utilised a widely used CT assessment framework at a school level. Findings: Many successes and challenges not reported in the literature on how CT can support learning outcomes in music and programming were revealed. Implications: Provided some significant limitations are considered, the major implication of the results suggest educators can use Sonic Pi to support many learning outcomes in music and programming for interested students.

Research on the Interaction of Genetic Algorithm in Assisted Composition.

Computer-aided composition is an attempt to use a formalized process to minimize human (or composer) involvement in the creation of music using a computer. Exploring the problem of computer-aided composition can enable us to understand and simulate the thinking mode of composers in the special process of music creation, which is an important application of artificial intelligence in the field of art. Feature extraction on the MIDI files has been introduced in this paper. Based on the genetic algorithm in this paper, a platform of the sampling coding method to optimize the character representation has solved the traditional algorithmic music composition study. Music directly from the pitch and duration can be derived from the characteristics, respectively, in the form of a one-hot encoding independently said. Failure to the rhythm of the characterization of the pitch and duration are problems that lead to the inability of compositional networks to learn musical styles better. Rhythm is the combination of pitch and time values according to certain rules. The rhythm of music affects the overall style of music. By associating the pitch and time value coding, the rhythm style of music can be preserved better so that the composition network can learn the style characteristics of music more easily.

Creativity in machines: Music composition using artificial intelligence

In this paper we propose a framework to take the next step towards making creative machines. Taking cue from Turing’s Mind Paper (1950) to more recent studies by Riedl in ‘’The Lovelace 2.0 test of artificial creativity and intelligence’ we try to examine a very creative area of human creativity – music. We have summarized the different works published on artificial intelligence and machine learning implemented for algorithmic music composition. Comparison of different algorithms-techniques including key features, advantages, disadvantages, common issues, trade-off and future aspects are discussed in detail. We then propose our own framework of how machines can be made to learn creativity.

Interactive sonification strategies for the motion and emotion of dance performances

Sonification has the potential to communicate a variety of data types to listeners including not just cognitive information, but also emotions and aesthetics. The goal of our dancer sonification project is to “sonify emotions as well as motions” of a dance performance via musical sonification. To this end, we developed and evaluated sonification strategies for adding a layer of emotional mappings to data sonification. Experiment 1 developed and evaluated four musical sonifications (i.e., sin-ification, MIDI-fication, melody module, and melody and arrangement module) to see their emotional effects. Videos were recorded of a professional dancer interacting with each of the four musical sonification strategies. Forty-eight participants provided ratings of musicality, emotional expressivity, and sound-motion/emotion compatibility via an online survey. Results suggest that increasing musical mappings led to higher ratings for each dimension for dance-type gestures. Experiment 2 used the musical sonification framework to develop four sonification scenarios that aimed to communicate a target emotion (happy, sad, angry, and tender). Thirty participants compared four interactive sonification scenarios with four pre-composed dance choreographies featuring the same musical and gestural palettes. Both forced choice and multi-dimensional emotional evaluations were collected, as well as motion/emotion compatibility ratings. Results show that having both music and dance led to higher accuracy scores for most target emotions, compared to music or dance conditions alone. These findings can contribute to the fields of movement sonification, algorithmic music composition, as well as affective computing in general, by describing strategies for conveying emotion through sound.

Algorithmic music in Haskell (invited talk)

Functional programming is becoming increasingly popular in artistic areas such as algorithmic music composition. Euterpea and Kulitta are two libraries for working with music in Haskell. Euterpea is a library for representing and manipulating basic musical structures, and is useful both in a pedagogical setting to teach functional programming through the arts and as a tool to create complex pieces of algorithmic music. Kulitta is a framework for automated composition that addresses music at a more abstract level than Euterpea, capturing aspects of musical style through geometric models and probabilistic grammars. Both of these libraries leverage Haskell’s pure functional nature and strong type system to achieve versatile, yet concise designs that allow the creation of diverse and interesting music. Features from these libraries have also been integral in the design of newer systems for natural language processing and artificial intelligence in the musical domain. This talk will explore challenges presented by creating these kinds of domain-specific embedded languages for working with music, and how taking functional approaches to them yields elegant solutions.

Semiotics and memetics in algorithmic music composition

Abstract In this article, we investigate the circumstances under which the sound structures, which are products of algorithmic composition, are considered as ‘music’. The methods of analysis and evaluation of the music pieces that have derived from mathematical procedures are still under development. Is their ‘beauty’ hidden in the final outcome of the algorithm or in its conceptual background? What is more important: the creator’s approach or the listener’s perception? The answers we attempt to provide are revealed from the tools of semiotics and memetics. We study whether a sound structure, derivative of an algorithm, can act as a signifier of an aesthetic signified, forming a seme. Moreover, we examine whether that seme can be spread as a meme (a unit of cultural phenomena).

Affective brain–computer music interfacing

Objective. We aim to develop and evaluate an affective brain–computer music interface (aBCMI) for modulating the affective states of its users. Approach. An aBCMI is constructed to detect a user's current affective state and attempt to modulate it in order to achieve specific objectives (for example, making the user calmer or happier) by playing music which is generated according to a specific affective target by an algorithmic music composition system and a case-based reasoning system. The system is trained and tested in a longitudinal study on a population of eight healthy participants, with each participant returning for multiple sessions. Main results. The final online aBCMI is able to detect its users current affective states with classification accuracies of up to 65% (3 class, ) and modulate its user's affective states significantly above chance level . Significance. Our system represents one of the first demonstrations of an online aBCMI that is able to accurately detect and respond to user's affective states. Possible applications include use in music therapy and entertainment.

JythonMusic: An environment for teaching algorithmic music composition, dynamic coding and musical performativity

Abstract The practice of coding music live challenges computing conventions with regard to developmental agility. The computational representation of music likewise challenges musicians to articulate their practice in new ways. In this article we describe the development, teaching and use of the JythonMusic environment designed to meet these challenges head on. JythonMusic, written in Python, is an open source project for music making and creative programming activities intended for musicians and programmers, of all levels and backgrounds. JythonMusic supports algorithmic music composition, dynamic coding and musical performativity including live coding. This article examines the functionality of JythonMusic for composition and performance projects in the context of university courses that combine computer science and music. The advantages of this medium for music and programming instruction are demonstrated through several projects and a live coding case study, leading to a series of observations and proposals concerning the advantages of coding in music pedagogy.

Investigating Perceived Emotional Correlates of Rhythmic Density in Algorithmic Music Composition

Affective algorithmic composition is a growing field that combines perceptually motivated affective computing strategies with novel music generation. This article presents work toward the development of one application. The long-term goal is to develop a responsive and adaptive system for inducing affect that is both controlled and validated by biophysical measures. Literature documenting perceptual responses to music identifies a variety of musical features and possible affective correlations, but perceptual evaluations of these musical features for the purposes of inclusion in a music generation system are not readily available. A discrete feature, rhythmic density (a function of note duration in each musical bar, regardless of tempo), was selected because it was shown to be well-correlated with affective responses in existing literature. A prototype system was then designed to produce controlled degrees of variation in rhythmic density via a transformative algorithm. A two-stage perceptual evaluation of a stimulus set created by this prototype was then undertaken. First, listener responses from a pairwise scaling experiment were analyzed via Multidimensional Scaling Analysis (MDS). The statistical best-fit solution was rotated such that stimuli with the largest range of variation were placed across the horizontal plane in two dimensions. In this orientation, stimuli with deliberate variation in rhythmic density appeared farther from the source material used to generate them than from stimuli generated by random permutation. Second, the same stimulus set was then evaluated according to the order suggested in the rotated two-dimensional solution in a verbal elicitation experiment. A Verbal Protocol Analysis (VPA) found that listener perception of the stimulus set varied in at least two commonly understood emotional descriptors, which might be considered affective correlates of rhythmic density. Thus, these results further corroborate previous studies wherein musical parameters are monitored for changes in emotional expression and that some similarly parameterized control of perceived emotional content in an affective algorithmic composition system can be achieved and provide a methodology for evaluating and including further possible musical features in such a system. Some suggestions regarding the test procedure and analysis techniques are also documented here.

Algorithmic music composition using dynamic Markov chains and genetic algorithms

Music has always been considered to be something uniquely human because of the creativity involved in its composition. Although this is the case, almost all composed music follows a set of rules ...

Algorithmic music composition as an introduction to computing

Educators have long debated the best way to introduce programming concepts. Should we teach using objects or functions or web page design? Should we use Java or Basic or HTML or Python? Should we s...

Neural Network Music Composition by Prediction: Exploring the Benefits of Psychoacoustic Constraints and Multi-scale Processing

Abstract In algorithmic music composition, a simple technique involves selecting notes sequentially according to a transition table that specifies the probability of the next note as a function of the previous context. An extension of this transition-table approach is described, using a recurrent autopredictive connectionist network called CONCERT. CONCERT is trained on a set of pieces with the aim of extracting stylistic regularities. CONCERT can then be used to compose new pieces. A central ingredient of CONCERT is the incorporation of psychologically grounded representations of pitch, duration and harmonic structure. CONCERT was tested on sets of examples artificially generated according to simple rules and was shown to learn the underlying structure, even where other approaches failed. In larger experiments, CONCERT was trained on sets of J. S. Bach pieces and traditional European folk melodies and was then allowed to compose novel melodies. Although the compositions are occasionally pleasant, and are preferred over compositions generated by a third-order transition table, the compositions suffer from a lack of global coherence. To overcome this limitation, several methods are explored to permit CONCERT to induce structure at both fine and coarse scales. In experiments with a training set of waltzes, these methods yielded limited success, but the overall results cast doubt on the promise of note-by-note prediction for composition.