Singing Voice Synthesis

This paper presents CrossSinger, a cross-lingual singing voice synthesizer based on Xiaoicesing2. It tackles the challenge of creating a multi-singer high-fidelity singing voice synthesis system with cross-lingual capabilities using only monolingual singers during training. The system unifies language representation, incorporates language information, and removes singer biases. Experimental results show that CrossSinger can synthesize high-quality songs for different singers in various languages, including code-switching cases.

This paper introduces HiFi-WaveGAN, a system designed for real-time synthesis of high-quality 48kHz singing voices from full-band mel-spectrograms. It improves upon WaveNet with a generator, incorporates elements from HiFiGAN and UnivNet, and introduces an auxiliary spectrogram-phase loss to enhance high-frequency reconstruction and accelerate training. HiFi-WaveGAN outperforms other neural vocoders like Parallel WaveGAN and HiFiGAN in quality metrics, with faster training and better high-frequency modeling.

This paper presents XiaoiceSing2, an enhanced singing voice synthesis system that addresses over-smoothing issues in middle- and high-frequency areas of mel-spectrograms. It employs a generative adversarial network (GAN) with improved model architecture to capture finer details.