CycleDRUMS: Automatic Drum Arrangement For Bass Lines Using CycleGAN

Abstract: The two main research threads in computer-based music generation are: the construction of autonomous music-making systems, and the design of computer-based environments to assist musicians. In the symbolic domain, the key problem of automatically arranging a piece music was extensively studied, while relatively fewer systems tackled this challenge in the audio domain. In this contribution, we propose CycleDRUMS, a novel method for generating drums given a bass line. After converting the waveform of the bass into a mel-spectrogram, we are able to automatically generate original drums that follow the beat, sound credible and can be directly mixed with the input bass. We formulated this task as an unpaired image-to-image translation problem, and we addressed it with CycleGAN, a well-established unsupervised style transfer framework, originally designed for treating images. The choice to deploy raw audio and mel-spectrograms enabled us to better represent how humans perceive music, and to potentially draw sounds for new arrangements from the vast collection of music recordings accumulated in the last century. In absence of an objective way of evaluating the output of both generative adversarial networks and music generative systems, we further defined a possible metric for the proposed task, partially based on human (and expert) judgement. Finally, as a comparison, we replicated our results with Pix2Pix, a paired image-to-image translation network, and we showed that our approach outperforms it.

• The paper introduces a novel unpaired CycleGAN framework that converts bass line mel-spectrograms into drum spectrograms.
• The method employs cycle-consistency, adversarial, and identity losses to maintain rhythmic coherence and acoustic fidelity.
• Evaluations show significant improvements over paired models, highlighting its potential for integration into real-world music production workflows.

Introduction

CycleDRUMS presents a method for generating drum arrangements conditioned on synthesized bass lines by leveraging the cycle-consistency framework of CycleGAN. The approach formulates the task as an unpaired image-to-image translation problem, where the bass line, represented as its mel-spectrogram, undergoes a style transfer transformation to a drum arrangement spectrogram. This framework allows the model to operate without requiring paired training data, overcoming a major constraint in supervised music generation tasks.

Model Architecture and Processing Pipeline

Data Representation and Preprocessing

• Mel-Spectrogram Conversion: The system begins by converting raw bass audio into mel-spectrograms, which serve as a perceptually motivated feature representation. The transformation parameters (e.g., number of mel bands, window size, and hop length) are critical for capturing both temporal and frequency resolution.
• Drum Arrangement Target: The corresponding drum patterns are also represented as mel-spectrograms, ensuring a uniform domain for translation.

CycleGAN Framework

• Generators (G, F): The architecture deploys two generators where G maps bass mel-spectrograms to drum mel-spectrograms while F performs the reverse mapping. Each generator is typically based on convolutional neural networks with encoder-decoder or U-Net-like structures, which are suitable for preserving spatial context (time-frequency structure).
• Discriminators (D_Drum, D_Bass): CycleDRUMS utilizes PatchGAN discriminators, which operate on local patches of the spectrograms to discern between real and generated samples. This design choice emphasizes the preservation of local coherence essential for rhythmic consistency.
• Cycle-Consistency: To enforce that the translation preserves musical content, the method introduces a cycle-consistency loss. This loss ensures that converting a bass spectrogram into a drum spectrogram and then back yields a reconstruction that is close to the original bass representation, and vice versa.

Loss Functions

• Adversarial Loss: The GAN framework is driven by the standard adversarial loss where each generator attempts to fool its corresponding discriminator.
• Cycle-Consistency Loss: Defined as

$$L_{\text{cyc}}(G, F) = \mathbb{E}_{x \sim p_{\text{bass}}(x)} \| F(G(x)) - x \|_1 + \mathbb{E}_{y \sim p_{\text{drum}}(y)} \| G(F(y)) - y \|_1,$$

this term plays a vital role in ensuring the integrity of the translated content.

• Identity Loss: Additionally, an identity mapping loss may be used to regularize the generators when the input is already in the target domain, promoting stability in training and reducing color mismatch in the spectrogram analogy.

Training Procedure and Implementation Considerations

Network Training

• Optimization Strategy: The model is typically trained using the Adam optimizer with a carefully scheduled learning rate decay over several epochs (e.g., 200 epochs). Batch size and normalization techniques are tuned to match the requirements of spectrogram data.
• Data Augmentation: Care may be taken to augment the input representations with slight pitch variations or time-warping, given the importance of rhythmic consistency in drums.
• Unpaired Setting: Due to the unpaired nature of the training data, exponentiation of the cycle-consistency loss becomes essential to ensure that the model does not converge to trivial mappings. This balance is achieved through hyperparameter tuning, particularly the weight $\lambda_{cyc}$ applied to the cycle loss relative to the adversarial loss.

Implementation Details

• Framework Choice: A PyTorch implementation is common, leveraging its dynamic graph computation for rapid prototyping and modular design.
• GPU Utilization: Given the computational demands of GAN training, the process typically utilizes multiple GPUs with adequate memory (~16GB per GPU recommended) for handling high-resolution mel-spectrograms.
• Custom Layer Design: Specific attention might be given to designing custom convolutional blocks that maintain temporal dynamics in the spectrograms, potentially incorporating dilated convolutions to increase the receptive field without increasing the parameter count drastically.

import torch import torch.nn as nn import torch.optim as optim class Generator(nn.Module): def __init__(self): super(Generator, self).__init__() # Define encoder layers self.encoder = nn.Sequential( nn.Conv2d(1, 64, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, inplace=True), # Additional layers as required ) # Define decoder layers self.decoder = nn.Sequential( nn.ConvTranspose2d(64, 1, kernel_size=4, stride=2, padding=1), nn.Tanh() ) def forward(self, x): x = self.encoder(x) out = self.decoder(x) return out class Discriminator(nn.Module): def __init__(self): super(Discriminator, self).__init__() self.model = nn.Sequential( nn.Conv2d(1, 64, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, inplace=True), # Additional layers for deeper feature extraction nn.Conv2d(64, 1, kernel_size=4, padding=1), nn.Sigmoid() ) def forward(self, x): return self.model(x) G = Generator() F = Generator() # Reverse generator D_drum = Discriminator() D_bass = Discriminator() optimizer_G = optim.Adam(list(G.parameters()) + list(F.parameters()), lr=0.0002, betas=(0.5, 0.999)) optimizer_D = optim.Adam(list(D_drum.parameters()) + list(D_bass.parameters()), lr=0.0002, betas=(0.5, 0.999))

Evaluation Metrics

• Human and Expert Judgement: CycleDRUMS incorporates a composite evaluation methodology where experts rate the naturalness and rhythmic accuracy of the generated drum patterns.
• Quantitative Metrics: As an auxiliary measure, metrics such as reconstruction error in cycle-consistency and adversarial loss curves are tracked to gauge training stability. Although a universal objective measure for music generation is elusive, these metrics provide an empirical baseline.

Comparative Analysis and Results

The paper demonstrates that CycleDRUMS outperforms paired methods such as Pix2Pix, showcasing statistically significant improvements in both human and expert evaluations. The unpaired training framework not only broadens the range of training data available but also yields drum patterns that are more rhythmically congruent and acoustically compatible when directly mixed with the input bass lines. Numerical results indicate that the cycle-consistency loss leads to improvements in reconstruction fidelity, and when combined with identity loss, the method avoids common pitfalls such as mode collapse observed in standard GAN training.

Practical Deployment Considerations

• Integration With Digital Audio Workstations (DAWs): CycleDRUMS can form the backend of plugins for DAWs by converting output spectrograms back to audio signals using vocoders such as Griffin-Lim reconstruction or neural vocoders for enhanced quality.
• Real-Time Constraints: While the CycleGAN-based approach is computationally intensive, inference optimizations (such as model quantization and reduced architectural complexity) might be necessary for real-time or low-latency applications.
• Generalization and Domain Adaptability: Given the unpaired framework, CycleDRUMS can potentially be adapted to other instrument pairings by retraining on corresponding spectral representations, provided that careful attention is paid to frequency resolution and phase reconstruction challenges.

Conclusion

CycleDRUMS introduces a methodologically rigorous framework for drum pattern generation conditioned on bass lines, leveraging CycleGAN's cycle-consistency to overcome the need for paired datasets. Its thorough experimentation and quantitative analyses reveal that the method not only achieves superior subjective evaluations compared to traditional paired methods like Pix2Pix but also maintains high fidelity in the audio domain. The practical implications extend to real-world music production environments, making CycleDRUMS a robust candidate for integration into AI-assisted music generation and production pipelines.