Zobrazeno 1 - 10
of 541
pro vyhledávání: '"Plumbley, Mark D."'
The broadcasting industry is increasingly adopting IP techniques, revolutionising both live and pre-recorded content production, from news gathering to live music events. IP broadcasting allows for the transport of audio and video signals in an easil
Externí odkaz:
http://arxiv.org/abs/2407.15423
Significant improvement has been achieved in automated audio captioning (AAC) with recent models. However, these models have become increasingly large as their performance is enhanced. In this work, we propose a knowledge distillation (KD) framework
Externí odkaz:
http://arxiv.org/abs/2407.14329
Autor:
Zhao, Junqi, Liu, Xubo, Zhao, Jinzheng, Yuan, Yi, Kong, Qiuqiang, Plumbley, Mark D., Wang, Wenwu
Universal sound separation (USS) is a task of separating mixtures of arbitrary sound sources. Typically, universal separation models are trained from scratch in a supervised manner, using labeled data. Self-supervised learning (SSL) is an emerging de
Externí odkaz:
http://arxiv.org/abs/2407.11745
Autor:
Yuan, Yi, Jia, Dongya, Zhuang, Xiaobin, Chen, Yuanzhe, Liu, Zhengxi, Chen, Zhuo, Wang, Yuping, Wang, Yuxuan, Liu, Xubo, Kang, Xiyuan, Plumbley, Mark D., Wang, Wenwu
Generative models have shown significant achievements in audio generation tasks. However, existing models struggle with complex and detailed prompts, leading to potential performance degradation. We hypothesize that this problem stems from the simpli
Externí odkaz:
http://arxiv.org/abs/2407.04416
Large language models (LLMs) have significantly advanced audio processing through audio codecs that convert audio into discrete tokens, enabling the application of language modelling techniques to audio data. However, traditional codecs often operate
Externí odkaz:
http://arxiv.org/abs/2405.00233
Autor:
Yuan, Yi, Chen, Zhuo, Liu, Xubo, Liu, Haohe, Xu, Xuenan, Jia, Dongya, Chen, Yuanzhe, Plumbley, Mark D., Wang, Wenwu
Contrastive language-audio pretraining~(CLAP) has been developed to align the representations of audio and language, achieving remarkable performance in retrieval and classification tasks. However, current CLAP struggles to capture temporal informati
Externí odkaz:
http://arxiv.org/abs/2404.17806
Autor:
Liang, Jinhua, Zhang, Huan, Liu, Haohe, Cao, Yin, Kong, Qiuqiang, Liu, Xubo, Wang, Wenwu, Plumbley, Mark D., Phan, Huy, Benetos, Emmanouil
We introduce WavCraft, a collective system that leverages large language models (LLMs) to connect diverse task-specific models for audio content creation and editing. Specifically, WavCraft describes the content of raw audio materials in natural lang
Externí odkaz:
http://arxiv.org/abs/2403.09527
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
Environment shifts and conflicts present significant challenges for learning-based sound event localization and detection (SELD) methods. SELD systems, when trained in particular acoustic settings, often show restricted generalization capabilities fo
Externí odkaz:
http://arxiv.org/abs/2312.16422
The auditory system plays a substantial role in shaping the overall human perceptual experience. While prevailing large language models (LLMs) and visual language models (VLMs) have shown their promise in solving a wide variety of vision and language
Externí odkaz:
http://arxiv.org/abs/2312.00249