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- W2919238531 abstract "We conduct an investigation on various hyper-parameters regarding neural networks used to generate spectral envelopes for singing synthesis. Two perceptive tests, where the first compares two models directly and the other ranks models with a mean opinion score, are performed. With these tests we show that when learning to predict spectral envelopes, 2d-convolutions are superior over previously proposed 1d-convolutions and that predicting multiple frames in an iterated fashion during training is superior over injecting noise to the input data. An experimental investigation whether learning to predict a probability distribution vs. single samples was performed but turned out to be inconclusive. A network architecture is proposed that incorporates the improvements which we found to be useful and we show in our experiments that this network produces better results than other stat-of-the-art methods." @default.
- W2919238531 created "2019-03-11" @default.
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- W2919238531 date "2019-03-01" @default.
- W2919238531 modified "2023-09-25" @default.
- W2919238531 title "Analysing Deep Learning-Spectral Envelope Prediction Methods for Singing Synthesis" @default.
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