4 accepted papers
Micron-scale robots (<i>μ</i>bots) have recently shown great promise for emerging medical applications. Accurate control of <i>μ</i>bots, while critical to their successful deployment, is challenging. In this work, we consider the problem of tracking a reference trajectory using a <i>μ</i>bot in the
In recent years, rapid progress has been made on the problem of single-channel sound separation using supervised training of deep neural networks. In such supervised approaches, a model is trained to predict the component sources from synthetic mixtures created by adding up isolated ground-truth sou…
We present an extension to the Tacotron speech synthesis architecture that learns a latent embedding space of prosody, derived from a reference acoustic representation containing the desired prosody. We show that conditioning Tacotron on this learned embedding space results in synthesized audio that…
We describe a neural network-based system for text-to-speech (TTS) synthesis that is able to generate speech audio in the voice of many different speakers, including those unseen during training. Our system consists of three independently trained components: (1) a speaker encoder network, trained on…