Bass Accompaniment Generation via Latent Diffusion
(2402.01412)Abstract
The ability to automatically generate music that appropriately matches an arbitrary input track is a challenging task. We present a novel controllable system for generating single stems to accompany musical mixes of arbitrary length. At the core of our method are audio autoencoders that efficiently compress audio waveform samples into invertible latent representations, and a conditional latent diffusion model that takes as input the latent encoding of a mix and generates the latent encoding of a corresponding stem. To provide control over the timbre of generated samples, we introduce a technique to ground the latent space to a user-provided reference style during diffusion sampling. For further improving audio quality, we adapt classifier-free guidance to avoid distortions at high guidance strengths when generating an unbounded latent space. We train our model on a dataset of pairs of mixes and matching bass stems. Quantitative experiments demonstrate that, given an input mix, the proposed system can generate basslines with user-specified timbres. Our controllable conditional audio generation framework represents a significant step forward in creating generative AI tools to assist musicians in music production.
Overview
-
The paper presents an AI system for automatically generating bass accompaniments that harmonize with existing musical mixes using audio autoencoders and a latent diffusion model.
-
Audio autoencoders compress audio waveforms into a high-fidelity, manageable latent space while preserving significant qualities of the audio.
-
The conditional latent diffusion model built on a U-Net architecture and self-attention mechanisms allows for the generation of basslines that match a given input and can be applied to music of any length.
-
The system offers control over the style of generated basslines, enabling users to specify timbre and style during generation.
-
Experiments demonstrate the model's ability to produce basslines that are musically coherent with the mix and stylistically accurate, with potential to extend the technology to other instruments.
Introduction
In the realm of music production, the generation of accompanying instrumentation using AI has rapidly become a sophisticated area of research. A seminal work in this field focuses on a system designed for the automatic creation of bass accompaniments that harmonize with an existing musical mix irrespective of the mix's complexity or length. The cornerstone of this approach is a blend of audio autoencoders for hyper-efficient compression of audio into invertible latent representations and a conditional latent diffusion model adept at basing its output on these compressed representations.
Audio Autoencoding and Latent Space Generation
To efficiently process the vast dimensionality of audio waveforms, this system employs audio autoencoders that reduce audio samples to a more manageable, condensed latent space. These autoencoders maintain a reconstruction quality high enough for audio while achieving significant compression ratios. Coupled with a novel architecture, the autoencoders generate compressed latent representations while adopting adversarial and multi-scale spectral distance losses to enhance their fidelity, particularly within perceptually salient regions of the audio spectrum.
Conditional Latent Diffusion Model
Central to this approach is the latent diffusion model, a generative model that facilitates conditional sampling. It excels in generating supplementary basslines that seamlessly dovetail with a given audio input. The model is built on a U-Net architecture and incorporates self-attention mechanisms, ensuring scalability to vary input and output lengths. Dynamic Positional Bias (DPB) is introduced to the attention mechanism to endow the model with the ability to process music pieces of arbitrary length, which is vital for real-world applications.
Controllability and Style Grounding
Controllability is at the forefront of this research, manifested through features like style conditioning and grounding in latent space. The paper asserts that their model can conceive basslines in line with a user-specified timbre or style by effectively projecting the desired characteristics onto the latent samples during generation. Numerical evaluations back these claims, where the system demonstrates the aptitude to output basslines that are congruent with input mixes and yet reflect the requested timbral qualities.
Experiments and Impact
Rigorous experiments underscore the system's efficacy. When benchmarked on a dataset of songs with distinct bass parts, the model showcases its ability to faithfully recreate basslines that musically align with the given mix and bear the hallmarks of the targeted style. A quantitative measure—through distance metrics in an embedding space—further confirms that the generated basslines resonate closely with the requisite style when compared to an ungrounded approach.
Conclusion
This research marks a prodigious leap in the application of generative AI for music creation, offering a dual advantage: generation aligned with existing musical elements and user-guided aesthetic control over the output. While the system mainly targets bass accompaniment, its impact could extend to other instrumental realms, emphasizing its potential in augmenting artistic creativity and streamlining music production workflows. As the paper concludes, future avenues could include expanding the model's repertoire to comprise a broader range of instruments, fortifying its position as a versatile and invaluable tool for musicians and producers alike.
Create an account to read this summary for free: