Music Composition with Deep Learning: A Review (2108.12290v2)

Abstract: Generating a complex work of art such as a musical composition requires exhibiting true creativity that depends on a variety of factors that are related to the hierarchy of musical language. Music generation have been faced with Algorithmic methods and recently, with Deep Learning models that are being used in other fields such as Computer Vision. In this paper we want to put into context the existing relationships between AI-based music composition models and human musical composition and creativity processes. We give an overview of the recent Deep Learning models for music composition and we compare these models to the music composition process from a theoretical point of view. We have tried to answer some of the most relevant open questions for this task by analyzing the ability of current Deep Learning models to generate music with creativity or the similarity between AI and human composition processes, among others.

• The paper reviews deep learning approaches, highlighting their strengths and limitations in automating music composition.
• It details the application of LSTM, VAE, and GAN models to generate coherent musical phrases and explore diverse musical styles.
• The paper underscores challenges in capturing the emotional depth of human music while suggesting future research directions for enhanced AI creativity.

Music Composition with Deep Learning: A Review

The paper "Music Composition with Deep Learning: A Review" by Carlos Hernandez-Olivan and Jose R. Beltran provides a comprehensive analysis of the deployment of deep learning techniques in the context of automated music composition. Through their examination, the authors attempt to bridge the conceptual parallels between AI-driven music generation and the traditional processes inherent in human musical creativity and composition. This investigation is conducted through a methodical review of pertinent deep learning models, juxtaposed with theoretical music composition approaches, yielding insights into the capabilities and limitations of current AI technologies in this domain.

Overview of Deep Learning Models in Music Composition

The paper discusses several deep learning frameworks that have been applied to music generation, building upon methodologies successfully utilized in computer vision and other AI fields. These include Long Short-Term Memory (LSTM) networks, Variational Autoencoders (VAE), and Generative Adversarial Networks (GAN), among others. The authors describe how these models leverage different strategies to tackle various aspects of music composition such as melody creation, instrumentation, and structure representation.

LSTM networks are highlighted for their ability to handle sequential data, capturing long-term dependencies necessary for producing coherent musical phrases. Meanwhile, VAEs contribute to generating novel compositions by learning latent representations of musical styles, affording a mechanism to produce diverse variations. GANs, with their adversarial training paradigm, are noted for advancing capabilities in generating high-quality symbolic music compositions.

Comparative Analysis of AI and Human Composition

The paper critically examines how AI can emulate aspects of human creativity in music. Human composition processes are inherently hierarchical and context-dependent, driven by emotional and cultural stimuli, factors that are yet challenging to model computationally. The authors scrutinize current AI models against these processes, finding that while AI offers significant enhancements in speed and the ability to create on-demand, it still struggles with comprehensively replicating the depth of human creativity.

The paper addresses open questions about deep learning's ability to generate "creative" music. Although AI systems can produce music that adheres to stylistic constraints, the measure of creativity from the human perspective, involving nuance and emotional depth, remains an elusive target for AI.

Evaluation and Implications

Assessing the outputs of AI-based music composition models remains complex. Various evaluation methods, both quantitative and qualitative, are highlighted. However, the subjective nature of music appreciation and creativity poses challenges in establishing standardized evaluation metrics. Implications of this research extend to practical applications in music production, educational tools for music theory, and enhancing interactive AI systems' capabilities.

The paper suggests that future research directions may include improving models' context-awareness and emotional quality of music generated through reinforcement learning and integrating more sophisticated music theories. Moreover, exploring human-AI collaborative frameworks could unleash new potentials in creative domains, leveraging the strengths of both AI computation capabilities and human artistic intuitiveness.

Conclusion

This paper contributes to the growing body of literature on AI-driven music composition, providing a detailed evaluation of current technologies and their relative efficacy in approximating human creativity. The ongoing advancements in deep learning models, as expounded in this paper, highlight both the milestones achieved and the complex challenges that persist in the field of creative AI applications. Future developments in this field could pave the way for more intuitive music creation tools, offering novel interactions between human composers and artificial intelligence.