GANSYNTH: ADVERSARIAL NEURAL AUDIO SYNTHESIS
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 but have slow iterative sampling and lack global latent structure. In contrast, 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.
This paper proposes a strategy to generate audio samples from noise with GANs. The treatment is analogous to image generation with GANs, with the emphasis being the changes to the architecture and representation necessary to make it possible to generate convincing audio that contains an interpretable latent code and is much faster than an autoregressive Wavenet based model ("Neural Audio Synthesis of Musical Notes with WaveNet AutoEncoders" - Engel et al (2017)). Like the other two related works (WaveGAN - "Adversarial Audio Synthesis" - Donahue et al 2018) and the Wavenet model above, it uses the NSynth dataset for its experiments. 
Much of the discussion is on the representation itself - in that, it is argued that using audio (WaveGAN) and log magnitude/phase spectrograms  (PhaseGAN) produce poorer results as compared with the version with the unrolled phase that they call 'IF' GANs, with high frequency resolution and log scaling to separate scales.  
The architecture of the network is similar to the recently published paper  (Donahue et al 2018), with convolutions and transpose convolutions adapted for audio. However, there seem to be two important developments. The current paper uses progressive growing of GANs (the current state of the art for producing high resolution images), and pitch conditioning (Odena et al, where labels are used to help training dynamics). 
For validation, the paper presents several metrics, with the recently proposed "NDB" metric figuring in the evaluations, which I think is interesting. The IF-Mel + high frequency resolution model seems to outperform the others in most of the evaluations, with good phase coherence and interpolation between latent codes. 
My thoughts: 
Overall, it seems that the paper's contributions are centered around the representation (with "IF-Mel" being the best). The architecture itself is not very different from commonly used DCGAN variants - the authors say that using PGGAN is desirable, but not critical, and the use of labels from Odena et al. 
Many of my own experiments with GANs were plagued by instability (especially at higher resolution) and mode collapse problems without special treatment (largely documented, such as adding noise, adjusting learning rates and so forth). To this end, what do the authors see as 'high' resolution vis a vis audio signals? 
I am curious if we can adapt these ideas for recurrent generators as might appear in TTS problems. 
I rate this paper as an accept since this is one of the few existing works that demonstrate successful audio generation from noise using GANs, and  owing to its novelty in exploring representation for audio.