1 citations · 3 across the 21 of their papers we have counts for
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Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering
Junyu Dai, Xinyue Fan, Weiqin Li +14
In this report, we present a unified song generation framework capable of producing high-quality full-length music from lyrics, text descriptions, and musical attributes. The propo…
InspireMusic: Integrating Super Resolution and Large Language Model for High-Fidelity Long-Form Music Generation
Chong Zhang, Yukun Ma, Qian Chen +12
We introduce InspireMusic, a framework integrated super resolution and large language model for high-fidelity long-form music generation. A unified framework generates high-fidelit…
Conditional Latent Diffusion-Based Speech Enhancement Via Dual Context Learning
Shengkui Zhao, Zexu Pan, Kun Zhou +3
Recently, the application of diffusion probabilistic models has advanced speech enhancement through generative approaches. However, existing diffusion-based methods have focused on…
HiFi-SR: A Unified Generative Transformer-Convolutional Adversarial Network for High-Fidelity Speech Super-Resolution
Shengkui Zhao, Kun Zhou, Zexu Pan +3
The application of generative adversarial networks (GANs) has recently advanced speech super-resolution (SR) based on intermediate representations like mel-spectrograms. However, e…
MossFormer2: Combining Transformer and RNN-Free Recurrent Network for Enhanced Time-Domain Monaural Speech Separation
Shengkui Zhao, Yukun Ma, Chongjia Ni +7
Our previously proposed MossFormer has achieved promising performance in monaural speech separation. However, it predominantly adopts a self-attention-based MossFormer module, whic…
Are Soft Prompts Good Zero-shot Learners for Speech Recognition?
Dianwen Ng, Chong Zhang, Ruixi Zhang +7
Large self-supervised pre-trained speech models require computationally expensive fine-tuning for downstream tasks. Soft prompt tuning offers a simple parameter-efficient alternati…