Zobrazeno 1 - 10
of 289
pro vyhledávání: '"Li Chenxing"'
Autor:
Li Chenxing
Publikováno v:
SHS Web of Conferences, Vol 188, p 01007 (2024)
In the era of big data, the financial landscape is undergoing transformative changes through the integration of advanced technologies. This research delves into the dynamic realm of asset portfolios within the information technology industry, traditi
Externí odkaz:
https://doaj.org/article/4fc0dde5ebbd43c5979f91bac3bf616c
Publikováno v:
Dizhi lixue xuebao, Vol 28, Iss 1, Pp 113-125 (2022)
Due to the complicated tectonic and sedimentary history and the lack of effective paleo-thermal indicators, the Meso-Neoproterozoic thermal history of the Yanliao rift zone in the northern margin of the North China Craton is ambiguous, which causes t
Externí odkaz:
https://doaj.org/article/b546eb4feee04753b33263aab9bdc323
Publikováno v:
Dizhi lixue xuebao, Vol 27, Iss 6, Pp 975-986 (2021)
Basin modeling is an essential technical method for the exploration and assessment of petroleum basins. However, traditional 2D basin modeling technologies only apply to extensional basins. This makes the thermal history reconstruction in lateral dir
Externí odkaz:
https://doaj.org/article/073b573b4f0f43a9b85e0f997b6d7a31
We introduce SRC-gAudio, a novel audio generation model designed to facilitate text-to-audio generation across a wide range of sampling rates within a single model architecture. SRC-gAudio incorporates the sampling rate as part of the generation cond
Externí odkaz:
http://arxiv.org/abs/2410.06544
With recent advances of AIGC, video generation have gained a surge of research interest in both academia and industry (e.g., Sora). However, it remains a challenge to produce temporally aligned audio to synchronize the generated video, considering th
Externí odkaz:
http://arxiv.org/abs/2409.14709
Autor:
Wang, Zhiyong, Fu, Ruibo, Wen, Zhengqi, Tao, Jianhua, Wang, Xiaopeng, Xie, Yuankun, Qi, Xin, Shi, Shuchen, Lu, Yi, Liu, Yukun, Li, Chenxing, Liu, Xuefei, Li, Guanjun
Speech synthesis technology has posed a serious threat to speaker verification systems. Currently, the most effective fake audio detection methods utilize pretrained models, and integrating features from various layers of pretrained model further enh
Externí odkaz:
http://arxiv.org/abs/2409.11909
Autor:
Qi, Xin, Fu, Ruibo, Wen, Zhengqi, Wang, Tao, Qiang, Chunyu, Tao, Jianhua, Li, Chenxing, Lu, Yi, Shi, Shuchen, Wang, Zhiyong, Wang, Xiaopeng, Xie, Yuankun, Liu, Yukun, Liu, Xuefei, Li, Guanjun
In recent years, speech diffusion models have advanced rapidly. Alongside the widely used U-Net architecture, transformer-based models such as the Diffusion Transformer (DiT) have also gained attention. However, current DiT speech models treat Mel sp
Externí odkaz:
http://arxiv.org/abs/2409.11835
Latent diffusion models have shown promising results in text-to-audio (T2A) generation tasks, yet previous models have encountered difficulties in generation quality, computational cost, diffusion sampling, and data preparation. In this paper, we int
Externí odkaz:
http://arxiv.org/abs/2409.10819
We introduce Diffusion-based Audio Captioning (DAC), a non-autoregressive diffusion model tailored for diverse and efficient audio captioning. Although existing captioning models relying on language backbones have achieved remarkable success in vario
Externí odkaz:
http://arxiv.org/abs/2409.09401
Autor:
Xiong, Chenxu, Fu, Ruibo, Shi, Shuchen, Wen, Zhengqi, Tao, Jianhua, Wang, Tao, Li, Chenxing, Qiang, Chunyu, Xie, Yuankun, Qi, Xin, Li, Guanjun, Yang, Zizheng
Current mainstream audio generation methods primarily rely on simple text prompts, often failing to capture the nuanced details necessary for multi-style audio generation. To address this limitation, the Sound Event Enhanced Prompt Adapter is propose
Externí odkaz:
http://arxiv.org/abs/2409.09381