A Functional Taxonomy of Music Generation Systems

Digital advances have transformed the face of automatic music generation since its beginnings at the dawn of computing. Despite the many breakthroughs, issues such as the musical tasks targeted by different machines and the degree to which they succeed remain open questions. We present a functional taxonomy for music generation systems with reference to existing systems. The taxonomy organizes systems according to the purposes for which they were designed. It also reveals the inter-relatedness amongst the systems. This design-centered approach contrasts with predominant methods-based surveys and facilitates the identification of grand challenges to set the stage for new breakthroughs.Comment: survey, music generation, taxonomy, functional survey, survey, automatic composition, algorithmic compositio

On the Complex Network Structure of Musical Pieces: Analysis of Some Use Cases from Different Music Genres

This paper focuses on the modeling of musical melodies as networks. Notes of a melody can be treated as nodes of a network. Connections are created whenever notes are played in sequence. We analyze some main tracks coming from different music genres, with melodies played using different musical instruments. We find out that the considered networks are, in general, scale free networks and exhibit the small world property. We measure the main metrics and assess whether these networks can be considered as formed by sub-communities. Outcomes confirm that peculiar features of the tracks can be extracted from this analysis methodology. This approach can have an impact in several multimedia applications such as music didactics, multimedia entertainment, and digital music generation.Comment: accepted to Multimedia Tools and Applications, Springe

Algorithmic music composition: a survey

This paper surveys some of the methods used for algorithmic composition and their evolution during the last decades. Algorithmic composition was motivated by the natural need to assist and to develop the process of music creation. Techniques and applications of algorithmic composition are broad spectrum, ranging from methods that produce entire works with no human intervention, up to methods were both composer and computer work closely together in real-time. Common algorithms used for music composition are based in stochastic, deterministic, chaotic and artificial intelligence methods.N/

Learning Distributed Representations for Multiple-Viewpoint Melodic Prediction

The analysis of sequences is important for extracting in- formation from music owing to its fundamentally temporal nature. In this paper, we present a distributed model based on the Restricted Boltzmann Machine (RBM) for learning melodic sequences. The model is similar to a previous suc- cessful neural network model for natural language [2]. It is first trained to predict the next pitch in a given pitch se- quence, and then extended to also make use of information in sequences of note-durations in monophonic melodies on the same task. In doing so, we also propose an efficient way of representing this additional information that takes advantage of the RBM’s structure. Results show that this RBM-based prediction model performs better than previ- ously evaluated n-gram models and also outperforms them in certain cases. It is able to make use of information present in longer sequences more effectively than n-gram models, while scaling linearly in the number of free pa- rameters required

Computational Creativity and Music Generation Systems: An Introduction to the State of the Art

Computational Creativity is a multidisciplinary field that tries to obtain creative behaviors from computers. One of its most prolific subfields is that of Music Generation (also called Algorithmic Composition or Musical Metacreation), that uses computational means to compose music. Due to the multidisciplinary nature of this research field, it is sometimes hard to define precise goals and to keep track of what problems can be considered solved by state-of-the-art systems and what instead needs further developments. With this survey, we try to give a complete introduction to those who wish to explore Computational Creativity and Music Generation. To do so, we first give a picture of the research on the definition and the evaluation of creativity, both human and computational, needed to understand how computational means can be used to obtain creative behaviors and its importance within Artificial Intelligence studies. We then review the state of the art of Music Generation Systems, by citing examples for all the main approaches to music generation, and by listing the open challenges that were identified by previous reviews on the subject. For each of these challenges, we cite works that have proposed solutions, describing what still needs to be done and some possible directions for further research