Zobrazeno 1 - 10
of 319
pro vyhledávání: '"Shi, Dongyuan"'
Semantic communication (SemCom) has emerged as a new paradigm for 6G communication, with deep learning (DL) models being one of the key drives to shift from the accuracy of bit/symbol to the semantics and pragmatics of data. Nevertheless, DL-based Se
Externí odkaz:
http://arxiv.org/abs/2406.06644
Computation-efficient Virtual Sensing Approach with Multichannel Adjoint Least Mean Square Algorithm
Multichannel active noise control (ANC) systems are designed to create a large zone of quietness (ZoQ) around the error microphones, however, the placement of these microphones often presents challenges due to physical limitations. Virtual sensing te
Externí odkaz:
http://arxiv.org/abs/2405.14158
Active Noise Control (ANC) is a widely adopted technology for reducing environmental noise across various scenarios. This paper focuses on enhancing noise reduction performance, particularly through the refinement of signal quality fed into ANC syste
Externí odkaz:
http://arxiv.org/abs/2405.12496
Delayless noise control is achieved by our earlier generative fixed-filter active noise control (GFANC) framework through efficient coordination between the co-processor and real-time controller. However, the one-dimensional convolutional neural netw
Externí odkaz:
http://arxiv.org/abs/2402.09460
Autor:
Bai, Jisheng, Wang, Mou, Liu, Haohe, Yin, Han, Jia, Yafei, Huang, Siwei, Du, Yutong, Zhang, Dongzhe, Shi, Dongyuan, Gan, Woon-Seng, Plumbley, Mark D., Rahardja, Susanto, Xiang, Bin, Chen, Jianfeng
Acoustic scene classification (ASC) is a crucial research problem in computational auditory scene analysis, and it aims to recognize the unique acoustic characteristics of an environment. One of the challenges of the ASC task is the domain shift betw
Externí odkaz:
http://arxiv.org/abs/2402.02694
Autor:
Yang, Lvyang, Zhang, Jiankang, Li, Huaiqiang, Ren, Longfei, Yang, Chen, Wang, Jingyu, Shi, Dongyuan
The digitization of engineering drawings is crucial for efficient reuse, distribution, and archiving. Existing computer vision approaches for digitizing engineering drawings typically assume the input drawings have high quality. However, in reality,
Externí odkaz:
http://arxiv.org/abs/2312.13620
Measurement uncertainties, represented by cyber-attacks and data losses, seriously degrade the quality of power system measurements. Fortunately, the powerful generation ability of the denoising diffusion models can enable more precise measurement ge
Externí odkaz:
http://arxiv.org/abs/2312.04346
Traditional binary hard labels for sound event detection (SED) lack details about the complexity and variability of sound event distributions. Recently, a novel annotation workflow is proposed to generate fine-grained non-binary soft labels, resultin
Externí odkaz:
http://arxiv.org/abs/2311.14068
Autor:
Bai, Jisheng, Yin, Han, Wang, Mou, Shi, Dongyuan, Gan, Woon-Seng, Chen, Jianfeng, Rahardja, Susanto
Previous studies in automated audio captioning have faced difficulties in accurately capturing the complete temporal details of acoustic scenes and events within long audio sequences. This paper presents AudioLog, a large language models (LLMs)-power
Externí odkaz:
http://arxiv.org/abs/2311.12371
False Data Injection Attack (FDIA) has become a growing concern in modern cyber-physical power systems. Most existing FDIA detection techniques project the raw measurement data into a high-dimensional latent space to separate normal and attacked samp
Externí odkaz:
http://arxiv.org/abs/2310.10666