6 papers
LatentFlowSR: High-Fidelity Audio Super-Resolution via Noise-Robust Latent Flow Matching
Fei Liu, Yang Ai, Hui-Peng Du +2
Audio super-resolution aims to recover missing high-frequency details from bandwidth-limited low-resolution audio, thereby improving the naturalness and perceptual quality of the r…
An Ultra-Low-Bitrate Neural Speech Codec with Plain-to-Pseudo Synergistic Vector Quantization
Xiao-Hang Jiang, Yang Ai, Fei Liu +4
Most neural speech codecs use residual vector quantization (RVQ), in which later VQs contribute less but consume the same bitrate, leading to inefficiency. We propose P2PSynCodec,…
ParaGSE: Parallel Generative Speech Enhancement with Group-Vector-Quantization-based Neural Speech Codec
Fei Liu, Yang Ai
Recently, generative speech enhancement has garnered considerable interest; however, existing approaches are hindered by excessive complexity, limited efficiency, and suboptimal sp…
Universal Discrete-Domain Speech Enhancement
Fei Liu, Yang Ai, Ye-Xin Lu +3
In real-world scenarios, speech signals are inevitably corrupted by various types of interference, making speech enhancement (SE) a critical task for robust speech processing. Howe…
Neural Speech Separation with Parallel Amplitude and Phase Spectrum Estimation
Fei Liu, Yang Ai, Zhen-Hua Ling
This paper proposes APSS, a novel neural speech separation model with parallel amplitude and phase spectrum estimation. Unlike most existing speech separation methods, the APSS dis…
Improving Noise Robustness of LLM-based Zero-shot TTS via Discrete Acoustic Token Denoising
Ye-Xin Lu, Hui-Peng Du, Fei Liu +2
Large language model (LLM) based zero-shot text-to-speech (TTS) methods tend to preserve the acoustic environment of the audio prompt, leading to degradation in synthesized speech…