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In this paper, we present a novel audio synthesizer, CAESynth, based on aconditional autoencoder. CAESynth synthesizes timbre in real-time byinterpolating the reference sounds in their shared latent feature space, whilecontrolling a pitch independently. We show that training a conditionalautoencoder based on accuracy in timbre classification together withadversarial regularization of pitch content allows timbre distribution inlatent space to be more effective and stable for timbre interpolation and pitchconditioning. The proposed method is applicable not only to creation of musicalcues but also to exploration of audio affordance in mixed reality based onnovel timbre mixtures with environmental sounds. We demonstrate by experimentsthat CAESynth achieves smooth and high-fidelity audio synthesis in real-timethrough timbre interpolation and independent yet accurate pitch control formusical cues as well as for audio affordance with environmental sound. A Pythonimplementation along with some generated samples are shared online.

CAESynth: Real-Time Timbre Interpolation and Pitch Control with Conditional Autoencoders