Zobrazeno 11 - 20
of 242
pro vyhledávání: '"Benetos, Emmanouil"'
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:
Yuan, Ruibin, Lin, Hanfeng, Wang, Yi, Tian, Zeyue, Wu, Shangda, Shen, Tianhao, Zhang, Ge, Wu, Yuhang, Liu, Cong, Zhou, Ziya, Ma, Ziyang, Xue, Liumeng, Wang, Ziyu, Liu, Qin, Zheng, Tianyu, Li, Yizhi, Ma, Yinghao, Liang, Yiming, Chi, Xiaowei, Liu, Ruibo, Wang, Zili, Li, Pengfei, Wu, Jingcheng, Lin, Chenghua, Liu, Qifeng, Jiang, Tao, Huang, Wenhao, Chen, Wenhu, Benetos, Emmanouil, Fu, Jie, Xia, Gus, Dannenberg, Roger, Xue, Wei, Kang, Shiyin, Guo, Yike
While Large Language Models (LLMs) demonstrate impressive capabilities in text generation, we find that their ability has yet to be generalized to music, humanity's creative language. We introduce ChatMusician, an open-source LLM that integrates intr
Externí odkaz:
http://arxiv.org/abs/2402.16153
Algorithms for automatic piano transcription have improved dramatically in recent years due to new datasets and modeling techniques. Recent developments have focused primarily on adapting new neural network architectures, such as the Transformer and
Externí odkaz:
http://arxiv.org/abs/2402.01424
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
Autor:
Manco, Ilaria, Weck, Benno, Doh, SeungHeon, Won, Minz, Zhang, Yixiao, Bogdanov, Dmitry, Wu, Yusong, Chen, Ke, Tovstogan, Philip, Benetos, Emmanouil, Quinton, Elio, Fazekas, György, Nam, Juhan
We introduce the Song Describer dataset (SDD), a new crowdsourced corpus of high-quality audio-caption pairs, designed for the evaluation of music-and-language models. The dataset consists of 1.1k human-written natural language descriptions of 706 mu
Externí odkaz:
http://arxiv.org/abs/2311.10057
Deep learning models such as CNNs and Transformers have achieved impressive performance for end-to-end audio tagging. Recent works have shown that despite stacking multiple layers, the receptive field of CNNs remains severely limited. Transformers on
Externí odkaz:
http://arxiv.org/abs/2311.01526
Autor:
Li, Dichucheng, Ma, Yinghao, Wei, Weixing, Kong, Qiuqiang, Wu, Yulun, Che, Mingjin, Xia, Fan, Benetos, Emmanouil, Li, Wei
Instrument playing techniques (IPTs) constitute a pivotal component of musical expression. However, the development of automatic IPT detection methods suffers from limited labeled data and inherent class imbalance issues. In this paper, we propose to
Externí odkaz:
http://arxiv.org/abs/2310.09853
Autor:
Deng, Zihao, Ma, Yinghao, Liu, Yudong, Guo, Rongchen, Zhang, Ge, Chen, Wenhu, Huang, Wenhao, Benetos, Emmanouil
Publikováno v:
2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Large Language Models (LLMs) have shown immense potential in multimodal applications, yet the convergence of textual and musical domains remains not well-explored. To address this gap, we present MusiLingo, a novel system for music caption generation
Externí odkaz:
http://arxiv.org/abs/2309.08730
Recent developments in MIR have led to several benchmark deep learning models whose embeddings can be used for a variety of downstream tasks. At the same time, the vast majority of these models have been trained on Western pop/rock music and related
Externí odkaz:
http://arxiv.org/abs/2307.09795
Autor:
Ma, Yinghao, Yuan, Ruibin, Li, Yizhi, Zhang, Ge, Chen, Xingran, Yin, Hanzhi, Lin, Chenghua, Benetos, Emmanouil, Ragni, Anton, Gyenge, Norbert, Liu, Ruibo, Xia, Gus, Dannenberg, Roger, Guo, Yike, Fu, Jie
Self-supervised learning (SSL) has shown promising results in various speech and natural language processing applications. However, its efficacy in music information retrieval (MIR) still remains largely unexplored. While previous SSL models pre-trai
Externí odkaz:
http://arxiv.org/abs/2307.05161