2024年的ICASSP在韩国首尔江南区Coex Center会议中心举办,今年的会议主题为: Signal Processing: The Foundation for True Intelligence. 1.1 会议信息: 总投稿量:5896,其中中国占比52.6%,美国占比11.9% 有关大模型研究的论文显著增多,共16篇与LLM相关,48篇与Multimodal相关 1.2 Best Papers 奖项论文名称研究单位论文...
投稿范围广，接受率40+，正文短，还是ccf-b，毕业神会不过分吧。但一般来说最终有效稿件数都是低于投...
Implementation of the paperSAM-Deblur: Let Segment Anything Boost Image Deblurring(ICASSP2024) Siwei Li*,Mingxuan Liu*, Yating Zhang, Shu Chen,Haoxiang Li, Zifei Dou,Hong Chen [Project] [Paper] [BibTeX] Todo: Full code release with instruction ...
Code for ICASSP 2024 paper"Embedded Feature Similarity Optimization with Specific Parameter Initialization for 2D/3D Medical Image Registration" - m1nhengChen/SOPI
Then, i. develop a speech enhancement model that best meets the Contest Objective as described more fully atsignal_paperand ii. submit a ICASSP 2024 Grand Challenge paper viaMicrosoft Conference Management Toolkit(opens in new tab)(opens in new tab)which reports the computational complexi...
An official implementation of the ICASSP 2024 paper: Dual-Path TFC-TDF UNet for Music Source Separation - junyuchen-cjy/DTTNet-Pytorch
Pretrained GOPT Models: We provide three pretrained GOPT models trained with various GOP features. These models generally perform better than the results reported in the paper because we report mean result of 5 runs with different random seeds in the paper while release the best model. ...
The results of the network are shared in Section 4 of the paper. Following is the accuracy curve obtained after training the model on 40 mel-bin magnitude spectrograms, 20 sub-spectrogram size and 10 mel-bin hop-size (72.18%, average-best accuracy in three runs). ...
🌟 STaR (ICASSP 2024) 🎉 Update (Apr 12, 2024): Our new paper, STaR, has been selected asBest Student Paperin ICASSP 2024! 🎉 Check out our model's performance inSUPERB Leaderboard! STaR: Distilling Speech Temporal Relation for Lightweight Speech Self-Supervised Learning Models, ICASSP...
CREPE uses the model size that was reported in the paper by default, but can optionally use a smaller model for computation speed, at the cost of slightly lower accuracy. You can specify --model-capacity {tiny|small|medium|large|full} as the command line option to select a model with des...