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org © 2024 IJCRT | Volume 12, Issue 5 May 2024 | ISSN: 2320-2882

GENERATE MUSIC WITH VARIATIONAL

AUTOENCODER
D L SIRI, CHARITHA K, VARSHA K, D HARISA FAIZA
Department of Computer Science with Specialization in Artificial Intelligence and Machine Learning
Presidency University, Bangalore, India
DEEPTHI S, Assistant Professor, Presidency University, Bangalore, India

Abstract: This paper introduces a pioneering method for music generation employing Variational Autoencoder (VAE) architecture,
a powerful to Autoencoder of deep learning. Leveraging the VAE's capacity to learn latent representations of intricate data
distributions, our approach encodes symbolic music representations into continuous latent spaces, enabling the generation of diverse
and coherent musical sequences. Through training on a dataset of musical compositions, the VAE captures underlying structural
nuances and stylistic elements, facilitating the generation of novel musical pieces by sampling latent vectors and decoding them
into symbolic notation. We evaluate the efficacy of our methodology through quantitative metrics assessing diversity, coherence,
and stylistic fidelity, alongside qualitative human evaluations of the generated music. Our findings illustrate that the VAE-based
approach yields music compositions exhibiting both diversity and coherence, while maintaining fidelity to the stylistic attributes of
the training data, suggesting the potential of VAEs as a compelling tool for creative music composition. This research contributes
to the evolving landscape of deep learning in music generation, underscoring the promise of Variational Autoencoders in capturing
and generating intricate musical structures.

Index Terms: Variational Autoencoder, Music Generation, Deep Learning

INTRODUCTION
Music generation has long been a fascinating area of exploration, combining creativity with technology to produce novel
compositions. With the advent of deep learning techniques, particularly Variational Autoencoders (VAEs), there has been a surge of
interest in leveraging these tools for creative endeavors like music composition. This paper delves into the realm of generating music
using VAEs, aiming to contribute to the growing body of research at the intersection of artificial intelligence and music. By harnessing
the latent spaces learned by VAEs, we aim to create a system capable of producing diverse and coherent musical sequences while
preserving the stylistic attributes inherent in the training data. This introduction sets the stage for our exploration into the application
of VAEs in music generation, highlighting the potential for innovation and creativity in this domain.

Fig.1. ARCHITECTURE OF VARIATIONAL AUTOENCODER

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www.ijcrt.org © 2024 IJCRT | Volume 12, Issue 5 May 2024 | ISSN: 2320-2882
LITERATURE REVIEW

The intersection of deep learning and music generation has sparked significant interest, leading to the exploration of various
approaches within artificial intelligence for creative composition. Variational Autoencoders (VAEs) stand out as a prominent avenue,
as evidenced by studies such as [1] and [2], which highlight their ability to capture complex data distributions and generate diverse
musical sequences while preserving stylistic attributes. Moreover, beyond VAEs, other deep learning architectures have been
investigated, including recurrent neural networks (RNNs) for polyphonic music generation ([3]) and Generative Adversarial Networks
(GANs) for realistic musical output ([4]). Recent research has further broadened the scope of AI-driven music generation, with studies
like [5] exploring music conditioned on visual inputs and [6] introducing innovative systems for music-conditioned 3D dance
generation. Collectively, this diverse literature underscores the ongoing exploration of AI as a tool for artistic expression,
encompassing VAEs, RNNs, GANs, and multimodal frameworks, and paving the way for future advancements in creative
computational systems.

METHODOLOGY
The methodology for this research entails several pivotal stages in facilitating music generation through Variational
Autoencoders (VAEs) and other deep learning architectures. Initially, a diverse dataset of symbolic music representations
is assembled, spanning various genres and styles, serving as the training corpus. The VAE architecture is then implemented
and trained on this dataset, employing optimization techniques like stochastic gradient descent to learn latent
representations capturing the music's structural and stylistic nuances. Concurrently, alternative deep learning frameworks
such as recurrent neural networks (RNNs) and Generative Adversarial Networks (GANs) may be explored for comparison
or combined approaches. Evaluation of the generated music encompasses both quantitative metrics assessing diversity,
coherence, and fidelity, alongside qualitative human assessments to gauge subjective quality. Furthermore, potential
extensions involve experimenting with innovative methodologies, such as conditioning music generation on visual inputs
or integrating 3D dance generation, to explore the interdisciplinary facets of AI-driven creative processes. This
comprehensive methodology aims to advance the understanding and capabilities of AI in music composition while
fostering innovation and creativity in computational systems for artistic expression.

Fig.2b. Visualisation
Fig.2a. Performed exploratory data analysis

OUTCOMES

The outcomes of this research endeavor manifest in a diverse array of generated musical sequences crafted through Variational
Autoencoders (VAEs), recurrent neural networks (RNNs), Generative Adversarial Networks (GANs), and potentially other
architectures, each evaluated quantitatively for diversity, coherence, and stylistic fidelity, alongside qualitative human assessments
offering subjective perspectives on their quality and artistic merit. This comprehensive evaluation framework provides insights into
the efficacy of deep learning methodologies in capturing and reproducing musical characteristics, contributing to the advancement of
AI-driven music generation. These outcomes not only deepen academic understanding but also have practical implications, guiding
the refinement of algorithms and methodologies for creative composition. By bridging technology and the arts, this research stimulates
innovation, fostering new frontiers in computational creativity and musical expression.

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CONCLUSION
In conclusion, the exploration of music generation using variational autoencoders (VAEs) highlights the potential of this approach
in creating diverse and musically coherent compositions. Despite the challenges in encoding musical information and evaluating
the quality of generated music, VAEs offer a flexible framework for capturing the complex structure of musical data and generating
novel compositions. The synthesis of theoretical foundations, methodological approaches, and experimental insights presented in
this paper underscores the importance of further research and development in VAE-based music generation systems. Future work
should focus on refining encoding strategies, enhancing evaluation metrics, and exploring interdisciplinary collaborations to
advance the state-of-the-art in computational music creativity. Ultimately, the integration of VAEs into music generation
frameworks has the potential to revolutionize how we create, appreciate, and interact with music in the digital age.

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