6 citations · 6 across the 2 of their papers we have counts for
4 papers · 1 filter
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…
ESPnet-Codec: Comprehensive Training and Evaluation of Neural Codecs for Audio, Music, and Speech
Jiatong Shi, Jinchuan Tian, Yihan Wu +17
Neural codecs have become crucial to recent speech and audio generation research. In addition to signal compression capabilities, discrete codecs have also been found to enhance do…
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…