Li-Chia Yang

1.3k total citations
10 papers, 498 citations indexed

About

Li-Chia Yang is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Cognitive Neuroscience. According to data from OpenAlex, Li-Chia Yang has authored 10 papers receiving a total of 498 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Signal Processing, 6 papers in Computer Vision and Pattern Recognition and 3 papers in Cognitive Neuroscience. Recurrent topics in Li-Chia Yang's work include Music and Audio Processing (8 papers), Music Technology and Sound Studies (6 papers) and Speech and Audio Processing (3 papers). Li-Chia Yang is often cited by papers focused on Music and Audio Processing (8 papers), Music Technology and Sound Studies (6 papers) and Speech and Audio Processing (3 papers). Li-Chia Yang collaborates with scholars based in United States and Taiwan. Li-Chia Yang's co-authors include Yi‐Hsuan Yang, Hao‐Wen Dong, Wen-Yi Hsiao, Alexander Lerch, Szu-Yu Chou, Matthew Mattina, Paul N. Whatmough, Carl Jensen, Yi‐An Chen and Jen-Yu Liu and has published in prestigious journals such as Neural Computing and Applications, arXiv (Cornell University) and Proceedings of the AAAI Conference on Artificial Intelligence.

In The Last Decade

Li-Chia Yang

10 papers receiving 474 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Li-Chia Yang United States	8	367	339	163	123	22	10	498
Slim Essid France	15	534 1.5×	377 1.1×	67 0.4×	178 1.4×	45 2.0×	55	719
Samer Abdallah United Kingdom	12	421 1.1×	296 0.9×	168 1.0×	132 1.1×	62 2.8×	26	570
Yi Ren China	15	425 1.2×	232 0.7×	57 0.3×	435 3.5×	12 0.5×	40	724
Barry Vercoe United States	14	562 1.5×	519 1.5×	203 1.2×	78 0.6×	30 1.4×	29	660
Isabel Barbancho Spain	10	315 0.9×	267 0.8×	82 0.5×	41 0.3×	29 1.3×	67	399
Luís Gustavo Martins Portugal	8	383 1.0×	319 0.9×	121 0.7×	102 0.8×	39 1.8×	17	518
Ryuichi Oka Japan	7	663 1.8×	527 1.6×	109 0.7×	152 1.2×	22 1.0×	58	824
Zekeriya TÜFEKCİ Türkiye	11	569 1.6×	248 0.7×	72 0.4×	251 2.0×	13 0.6×	34	698
Ju-Chiang Wang Taiwan	14	374 1.0×	260 0.8×	130 0.8×	136 1.1×	22 1.0×	35	503
Nicholas J. Bryan United States	16	356 1.0×	230 0.7×	97 0.6×	109 0.9×	22 1.0×	32	479

Countries citing papers authored by Li-Chia Yang

Since Specialization

Citations

This map shows the geographic impact of Li-Chia Yang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Li-Chia Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Li-Chia Yang more than expected).

Fields of papers citing papers by Li-Chia Yang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Li-Chia Yang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Li-Chia Yang. The network helps show where Li-Chia Yang may publish in the future.

Co-authorship network of co-authors of Li-Chia Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Li-Chia Yang. A scholar is included among the top collaborators of Li-Chia Yang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Li-Chia Yang. Li-Chia Yang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

Yang, Li-Chia, et al.. (2023). Self-Supervised Learning for Speech Enhancement Through Synthesis. 1–5. 12 indexed citations

Liu, Yuchen, et al.. (2022). CCATMos: Convolutional Context-aware Transformer Network for Non-intrusive Speech Quality Assessment. Interspeech 2022. 3318–3322. 2 indexed citations

Jensen, Carl, et al.. (2020). TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids. arXiv (Cornell University). 4054–4058. 72 indexed citations

Dong, Hao‐Wen, Wen-Yi Hsiao, Li-Chia Yang, & Yi‐Hsuan Yang. (2018). MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 258 indexed citations

Yang, Li-Chia & Alexander Lerch. (2018). On the evaluation of generative models in music. Neural Computing and Applications. 32(9). 4773–4784. 84 indexed citations

Dong, Hao‐Wen, Wen-Yi Hsiao, Li-Chia Yang, & Yi‐Hsuan Yang. (2017). MuseGAN: Symbolic-domain Music Generation and Accompaniment with Multi-track Sequential Generative Adversarial Networks. arXiv (Cornell University). 14 indexed citations

Yang, Li-Chia, Szu-Yu Chou, & Yi‐Hsuan Yang. (2017). MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation using 1D and 2D Conditions.. arXiv (Cornell University). 13 indexed citations

Yang, Li-Chia, Szu-Yu Chou, & Yi‐Hsuan Yang. (2017). Midinet: A Convolutional Generative Adversarial Network For Symbolic-Domain Music Generation.. Zenodo (CERN European Organization for Nuclear Research). 324–331. 19 indexed citations

Chou, Szu-Yu, Li-Chia Yang, Yi‐Hsuan Yang, & Jyh‐Shing Roger Jang. (2017). Conditional preference nets for user and item cold start problems in music recommendation. abs 1411 1784. 1147–1152. 5 indexed citations

10.

Yang, Li-Chia, Szu-Yu Chou, Jen-Yu Liu, Yi‐Hsuan Yang, & Yi‐An Chen. (2017). Revisiting the problem of audio-based hit song prediction using convolutional neural networks. 621–625. 19 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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