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Found 8 papers, 4 papers with code
Semi-supervised Learning for Singing Synthesis Timbre
Our system is an encoder-decoder model with two encoders, linguistic and acoustic, and one (acoustic) decoder.
Content Based Singing Voice Extraction From a Musical Mixture
We present a deep learning based methodology for extracting the singing voice signal from a musical mixture based on the underlying linguistic content.
Sequence-to-sequence Singing Synthesis Using the Feed-forward Transformer
We propose a sequence-to-sequence singing synthesizer, which avoids the need for training data with pre-aligned phonetic and acoustic features.
WGANSing: A Multi-Voice Singing Voice Synthesizer Based on the Wasserstein-GAN
We present a deep neural network based singing voice synthesizer, inspired by the Deep Convolutions Generative Adversarial Networks (DCGAN) architecture and optimized using the Wasserstein-GAN algorithm.
Sound Audio and Speech Processing
WGANSing
We present a deep neural network based singing voice synthesizer, inspired by the Deep Convolutions Generative Adversarial Networks (DCGAN) architecture and optimized using the Wasserstein-GAN algorithm.
Data Efficient Voice Cloning for Neural Singing Synthesis
There are many use cases in singing synthesis where creating voices from small amounts of data is desirable.
Deep Learning for Singing Processing: Achievements, Challenges and Impact on Singers and Listeners
This paper summarizes some recent advances on a set of tasks related to the processing of singing using state-of-the-art deep learning techniques.
A Neural Parametric Singing Synthesizer
We present a new model for singing synthesis based on a modified version of the WaveNet architecture.
