This survey and benchmark of deep time series models using the released TSLib library finds that models with specific structures perform well only on distinct analysis tasks.
Samplernn: An unconditional end-to-end neural audio generation model
5 Pith papers cite this work. Polarity classification is still indexing.
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abstract
In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time. We show that our model, which profits from combining memory-less modules, namely autoregressive multilayer perceptrons, and stateful recurrent neural networks in a hierarchical structure is able to capture underlying sources of variations in the temporal sequences over very long time spans, on three datasets of different nature. Human evaluation on the generated samples indicate that our model is preferred over competing models. We also show how each component of the model contributes to the exhibited performance.
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A conditional GAN is used to synthesize speech waveforms from compressed glottal excitation, refined by LPC parameters, yielding higher quality reconstructions than traditional methods on a 30-speaker dataset.
Sparse Transformers factorize attention to handle sequences tens of thousands long, achieving new SOTA density modeling on Enwik8, CIFAR-10, and ImageNet-64.
Residual-domain F0 transformation generalizes spectrum-differential direct waveform modification to arbitrary spectral conversion models in voice conversion.
Chain-of-Details (CoD) is a cascaded TTS method that explicitly models temporal coarse-to-fine dynamics with a shared decoder, achieving competitive performance using significantly fewer parameters.
citing papers explorer
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Deep Time Series Models: A Comprehensive Survey and Benchmark
This survey and benchmark of deep time series models using the released TSLib library finds that models with specific structures perform well only on distinct analysis tasks.
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Analysis by Adversarial Synthesis -- A Novel Approach for Speech Vocoding
A conditional GAN is used to synthesize speech waveforms from compressed glottal excitation, refined by LPC parameters, yielding higher quality reconstructions than traditional methods on a 30-speaker dataset.
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Generating Long Sequences with Sparse Transformers
Sparse Transformers factorize attention to handle sequences tens of thousands long, achieving new SOTA density modeling on Enwik8, CIFAR-10, and ImageNet-64.
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Generalization of Spectrum Differential based Direct Waveform Modification for Voice Conversion
Residual-domain F0 transformation generalizes spectrum-differential direct waveform modification to arbitrary spectral conversion models in voice conversion.
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Text-To-Speech with Chain-of-Details: modeling temporal dynamics in speech generation
Chain-of-Details (CoD) is a cascaded TTS method that explicitly models temporal coarse-to-fine dynamics with a shared decoder, achieving competitive performance using significantly fewer parameters.