6 citations · 7 across the 13 of their papers we have counts for
4 papers · 2 filters
Codec-SUPERB @ SLT 2024: A lightweight benchmark for neural audio codec models
Haibin Wu, Xuanjun Chen, Yi-Cheng Lin +13
Neural audio codec models are becoming increasingly important as they serve as tokenizers for audio, enabling efficient transmission or facilitating speech language modeling. The i…
Leveraging Joint Spectral and Spatial Learning with MAMBA for Multichannel Speech Enhancement
Wenze Ren, Haibin Wu, Yi-Cheng Lin +7
In multichannel speech enhancement, effectively capturing spatial and spectral information across different microphones is crucial for noise reduction. Traditional methods, such as…
Towards audio language modeling -- an overview
Haibin Wu, Xuanjun Chen, Yi-Cheng Lin +4
Neural audio codecs are initially introduced to compress audio data into compact codes to reduce transmission latency. Researchers recently discovered the potential of codecs as su…
Codec-SUPERB: An In-Depth Analysis of Sound Codec Models
Haibin Wu, Ho-Lam Chung, Yi-Cheng Lin +7
The sound codec's dual roles in minimizing data transmission latency and serving as tokenizers underscore its critical importance. Recent years have witnessed significant developme…