107 research outputs found
Piano Genie
We present Piano Genie, an intelligent controller which allows non-musicians
to improvise on the piano. With Piano Genie, a user performs on a simple
interface with eight buttons, and their performance is decoded into the space
of plausible piano music in real time. To learn a suitable mapping procedure
for this problem, we train recurrent neural network autoencoders with discrete
bottlenecks: an encoder learns an appropriate sequence of buttons corresponding
to a piano piece, and a decoder learns to map this sequence back to the
original piece. During performance, we substitute a user's input for the
encoder output, and play the decoder's prediction each time the user presses a
button. To improve the intuitiveness of Piano Genie's performance behavior, we
impose musically meaningful constraints over the encoder's outputs.Comment: Published as a conference paper at ACM IUI 201
Expediting TTS Synthesis with Adversarial Vocoding
Recent approaches in text-to-speech (TTS) synthesis employ neural network
strategies to vocode perceptually-informed spectrogram representations directly
into listenable waveforms. Such vocoding procedures create a computational
bottleneck in modern TTS pipelines. We propose an alternative approach which
utilizes generative adversarial networks (GANs) to learn mappings from
perceptually-informed spectrograms to simple magnitude spectrograms which can
be heuristically vocoded. Through a user study, we show that our approach
significantly outperforms na\"ive vocoding strategies while being hundreds of
times faster than neural network vocoders used in state-of-the-art TTS systems.
We also show that our method can be used to achieve state-of-the-art results in
unsupervised synthesis of individual words of speech.Comment: Published as a conference paper at INTERSPEECH 201
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