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Generative models in vision have seen rapid progress due to algorithmicimprovements and the availability of high-quality image datasets. In thispaper, we offer contributions in both these areas to enable similar progress inaudio modeling. First, we detail a powerful new WaveNet-style autoencoder modelthat conditions an autoregressive decoder on temporal codes learned from theraw audio waveform. Second, we introduce NSynth, a large-scale and high-qualitydataset of musical notes that is an order of magnitude larger than comparablepublic datasets. Using NSynth, we demonstrate improved qualitative andquantitative performance of the WaveNet autoencoder over a well-tuned spectralautoencoder baseline. Finally, we show that the model learns a manifold ofembeddings that allows for morphing between instruments, meaningfullyinterpolating in timbre to create new types of sounds that are realistic andexpressive.

Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders