GANSynth: Adversarial Neural Audio Synthesis (1902.08710v2)

Abstract: Efficient audio synthesis is an inherently difficult machine learning task, as human perception is sensitive to both global structure and fine-scale waveform coherence. Autoregressive models, such as WaveNet, model local structure at the expense of global latent structure and slow iterative sampling, while Generative Adversarial Networks (GANs), have global latent conditioning and efficient parallel sampling, but struggle to generate locally-coherent audio waveforms. Herein, we demonstrate that GANs can in fact generate high-fidelity and locally-coherent audio by modeling log magnitudes and instantaneous frequencies with sufficient frequency resolution in the spectral domain. Through extensive empirical investigations on the NSynth dataset, we demonstrate that GANs are able to outperform strong WaveNet baselines on automated and human evaluation metrics, and efficiently generate audio several orders of magnitude faster than their autoregressive counterparts.

• The paper introduces a novel GAN-based method using spectral domain representations to generate high-fidelity audio.
• The study shows that modeling instantaneous frequencies results in audio with superior local coherence and is up to 54,000 times faster than WaveNet.
• The findings highlight GANs' potential for interactive, real-time audio applications, setting the stage for advanced synthesis techniques.

Adversarial Neural Audio Synthesis: A Study of GANSynth

This essay reviews the contributions and findings of the paper on GANSynth, an approach utilizing Generative Adversarial Networks (GANs) for high-fidelity audio synthesis, particularly focusing on music notes. The paper addresses the challenges inherent in audio synthesis, such as maintaining local waveform coherence while capturing global structure, a task made difficult by the temporal scale differences between audio waveforms and the computational feasibility of processing them.

The traditional approach employing autoregressive models like the WaveNet has shown promise by modeling audio at the most granular scale—individual samples—resulting in high-quality output. However, this methodology suffers from slow sample generation due to the iterative nature of processing each audio sample sequentially. Contrarily, GANs allow for parallel sampling and offer more efficient processing, yet they typically struggle with producing waveforms with sufficient local coherence.

This work intervenes by proposing a spectral domain representation method where GANs model log magnitudes and instantaneous frequencies. By exploring this approach in the context of the NSynth dataset, a collection focusing on isolated musical notes with diverse pitches and timbres, the paper demonstrates significant findings that offer new insights for the field of audio synthesis:

1. Audio Representation and Coherence: Different representation strategies were evaluated to address the challenge of local coherence. The findings suggest that generating log-magnitude spectrograms and instantaneous frequencies yields more coherent output than directly generating waveforms.
2. Performance Metrics: Through empirical studies, in particular, the models based on instantaneous frequencies outperformed those using phase-based representations in human evaluations and other metrics. Notably, the studied GAN variants were able to produce audio with fidelity comparable to or exceeding WaveNet baselines while surpassing them in terms of generation speed by orders of magnitude.
3. Technical Achievements: The findings highlighted that GANs could exploit high-frequency resolution representations to achieve substantial performance enhancements. The most effective GANSynth model achieved audio quality on a par with naturally occurring sounds in the dataset and demonstrated exceptional speed, generating audio approximately 54,000 times faster than WaveNet models.

The implications of GANSynth's success in audio generation suggest that GANs, when appropriately tuned and focused on suitable representations, offer substantial gains for interactive audio applications and real-time systems. The efficiency and quality of GAN-generated outputs pave the way for further explorations into domain transfer tasks and the synthesis of more complex auditory scenes, possibly extending into speech synthesis and sound design.

Future research could focus on addressing the remaining challenges, such as diversity in audio generation and potential extensions into less structured audio domains beyond isolated notes. Moreover, the combination of GANs with other generative model techniques could provide new pathways for enhancing both the fidelity and the diversity of audio generated in various practical applications. Additionally, potential applications in real-time systems underline the importance of further optimizing GAN training and representation methodologies to expand their capabilities in real-world scenarios.