S. Dieleman

Publications48
h-index26
Citations23,876
Highly Influential Citations1,898
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WaveNet: A Generative Model for Raw Audio
TLDR
WaveNet, a deep neural network for generating raw audio waveforms, is introduced; it is shown that it can be efficiently trained on data with tens of thousands of samples per second of audio, and can be employed as a discriminative model, returning promising results for phoneme recognition.
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Mastering the game of Go with deep neural networks and tree search
TLDR
Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.
View on Nature
Theano: A Python framework for fast computation of mathematical expressions
TLDR
The performance of Theano is compared against Torch7 and TensorFlow on several machine learning models and recently-introduced functionalities and improvements are discussed.
View PDF on arXiv
Parallel WaveNet: Fast High-Fidelity Speech Synthesis
The recently-developed WaveNet architecture is the current state of the art in realistic speech synthesis, consistently rated as more natural sounding for many different languages than any previous…
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Efficient Neural Audio Synthesis
TLDR
A single-layer recurrent neural network with a dual softmax layer that matches the quality of the state-of-the-art WaveNet model, the WaveRNN, and a new generation scheme based on subscaling that folds a long sequence into a batch of shorter sequences and allows one to generate multiple samples at once.
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Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders
TLDR
A powerful new WaveNet-style autoencoder model is detailed that conditions an autoregressive decoder on temporal codes learned from the raw audio waveform, and NSynth, a large-scale and high-quality dataset of musical notes that is an order of magnitude larger than comparable public datasets is introduced.
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Deep content-based music recommendation
TLDR
This paper proposes to use a latent factor model for recommendation, and predict the latent factors from music audio when they cannot be obtained from usage data, and shows that recent advances in deep learning translate very well to the music recommendation setting, with deep convolutional neural networks significantly outperforming the traditional approach.
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Lasagne: First release.
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Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset
TLDR
By using notes as an intermediate representation, a suite of models capable of transcribing, composing, and synthesizing audio waveforms with coherent musical structure on timescales spanning six orders of magnitude are trained, a process the authors call Wave2Midi2Wave.
View PDF on arXiv
End-to-end learning for music audio
TLDR
Although convolutional neural networks do not outperform a spectrogram-based approach, the networks are able to autonomously discover frequency decompositions from raw audio, as well as phase-and translation-invariant feature representations.
View on IEEE
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