C-RNN-GAN: Continuous recurrent neural networks with adversarial training (1611.09904v1)

Abstract: Generative adversarial networks have been proposed as a way of efficiently training deep generative neural networks. We propose a generative adversarial model that works on continuous sequential data, and apply it by training it on a collection of classical music. We conclude that it generates music that sounds better and better as the model is trained, report statistics on generated music, and let the reader judge the quality by downloading the generated songs.

• The paper introduces a novel C-RNN-GAN architecture that merges RNNs and GANs to generate continuous, polyphonic music sequences.
• It employs LSTM-based generators and discriminators using Backpropagation Through Time and feature matching for robust adversarial training.
• Experimental results show enhanced variability, improved polyphony, and realistic feature matching compared to traditional RNN models.

C-RNN-GAN: Continuous Recurrent Neural Networks with Adversarial Training

Overview

The paper introduces C-RNN-GAN, a pioneering architecture that combines recurrent neural networks (RNNs) with generative adversarial networks (GANs) to model and generate continuous sequential data such as music. Unlike traditional applications that use discrete symbolic representations, this model employs continuous representations of musical attributes—tone length, frequency, intensity, and timing. This research provides a novel approach to training deep generative models, particularly focusing on classical music datasets in MIDI format.

Technical Approach

The proposed C-RNN-GAN architecture consists of a generator (G) and a discriminator (D), both implemented using deep RNNs with Long Short-Term Memory (LSTM) units. The generator aims to produce music sequences indistinguishable from real data, while the discriminator distinguishes between the generated sequences and actual data. The loss functions for the generator and discriminator are formulated as a zero-sum game, striving towards a Nash equilibrium.

The architecture uniquely allows for the generation of polyphonic music by representing up to three tones per LSTM cell output as real-valued quadruplets. This representation supports continuous attribute variations, enabling the model to leverage backpropagation for end-to-end training efficiently.

Experimental Setup and Results

The model's efficacy was assessed using a dataset comprised of 3,697 MIDI files from 160 classical composers. The evaluation covered metrics like polyphony, scale consistency, tone repetitions, and tone span. Comparative analysis with a baseline RNN trained on maximum likelihood estimation highlighted the superiority of C-RNN-GAN in generating musically complex outputs.

Key findings include:

• Improved Variability: C-RNN-GAN demonstrated greater variability in tone span and intensity compared to the baseline which maintained higher tonal consistency but lacked dynamic range.
• Enhanced Polyphony: Allowing multiple tones per LSTM cell significantly improved polyphonic capabilities.
• Realistic Feature Matching: The use of feature matching in adversarial training increased the model's ability to capture realistic musical structures.

The training utilized techniques such as Backpropagation Through Time (BPTT) and feature matching to stabilize the adversarial process and enhance representational fidelity. Freezing technique was applied to manage discriminator strength, ensuring balanced adversarial progression.

Implications and Future Directions

C-RNN-GAN offers a significant contribution to the field of generative models for sequential data, highlighting the feasibility of modeling highly variable continuous sequences with adversarial training. The research opens pathways for further exploration in various domains beyond music, such as speech synthesis and financial predictions.

Future investigation could focus on broadening the versatility of the model to accommodate longer dependencies and more diverse datasets. The integration of curriculum learning proved beneficial; thus, adaptive learning strategies could be explored to improve convergence and stability further.

The potential for practical applications is vast, considering the increasing interest in AI-mediated compositions and interactive music systems. Continued enhancements could see this model applied to real-time music generation and personalized content creation platforms.

In summary, C-RNN-GAN provides a compelling framework for advancing neural network capabilities in generating complex, continuous sequences, setting a promising foundation for future research and application in artificial intelligence and music technology.