Jian Luan

412 total citations
22 papers, 169 citations indexed

About

Jian Luan is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Jian Luan has authored 22 papers receiving a total of 169 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 11 papers in Signal Processing and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Jian Luan's work include Speech Recognition and Synthesis (13 papers), Music and Audio Processing (11 papers) and Speech and Audio Processing (9 papers). Jian Luan is often cited by papers focused on Speech Recognition and Synthesis (13 papers), Music and Audio Processing (11 papers) and Speech and Audio Processing (9 papers). Jian Luan collaborates with scholars based in China, United States and Japan. Jian Luan's co-authors include Xu Tan, Tie‐Yan Liu, Yi Ren, Tao Qin, Xu Tan, Zhou Zhao, Jie Wu, Frank K. Soong, Shuo Shang and Shengchen Li and has published in prestigious journals such as ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

In The Last Decade

Jian Luan

16 papers receiving 161 citations

Peers

Jian Luan
Soham Deshmukh United Kingdom
Weiwei Lin Hong Kong
Xu Xiang China
Benoît Fauve United Kingdom
Mike Seltzer United States
Andrew Gibiansky United States
Marijn Huijbregts Netherlands
Soham Deshmukh United Kingdom
Jian Luan
Citations per year, relative to Jian Luan Jian Luan (= 1×) peers Soham Deshmukh

Countries citing papers authored by Jian Luan

Since Specialization
Citations

This map shows the geographic impact of Jian Luan'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 Jian Luan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jian Luan more than expected).

Fields of papers citing papers by Jian Luan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jian Luan. 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 Jian Luan. The network helps show where Jian Luan may publish in the future.

Co-authorship network of co-authors of Jian Luan

This figure shows the co-authorship network connecting the top 25 collaborators of Jian Luan. A scholar is included among the top collaborators of Jian Luan 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 Jian Luan. Jian Luan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
2.
Liu, Wei, et al.. (2024). MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI Understanding. 10231–10251. 4 indexed citations
3.
Liu, Wei, Jian Luan, Bin Wang, et al.. (2024). DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy. 9828–9862. 6 indexed citations
5.
Liu, Wei, et al.. (2024). A Comprehensive Evaluation of Quantization Strategies for Large Language Models. 12186–12215. 16 indexed citations
6.
Li, Ang, et al.. (2024). Mobile-Bench: An Evaluation Benchmark for LLM-based Mobile Agents. 8813–8831. 5 indexed citations
7.
Wu, Jie, Jian Luan, & Yujun Wang. (2023). LightClone: Speaker-guided Parallel Subnet Selection for Few-shot Voice Cloning. 4868–4872.
8.
Luan, Jian, et al.. (2022). Improving Emotional Speech Synthesis by Using SUS-Constrained VAE and Text Encoder Aggregation. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 8302–8306. 4 indexed citations
9.
Wu, Qinghua, et al.. (2022). MSDTRON: A High-Capability Multi-Speaker Speech Synthesis System for Diverse Data Using Characteristic Information. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 6327–6331. 2 indexed citations
10.
Luan, Jian, et al.. (2022). PAMA-TTS: Progression-Aware Monotonic Attention for Stable SEQ2SEQ TTS with Accurate Phoneme Duration Control. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 7467–7471. 1 indexed citations
12.
Luan, Jian, et al.. (2020). XiaoiceSing: A High-Quality and Integrated Singing Voice Synthesis System. 1306–1310. 44 indexed citations
13.
Soong, Frank K., et al.. (2020). Transfer Learning for Improving Singing-Voice Detection in Polyphonic Instrumental Music. 1236–1240. 7 indexed citations
14.
Zhang, Kun, et al.. (2020). Re-Weighted Interval Loss for Handling Data Imbalance Problem of End-to-End Keyword Spotting. 2567–2571. 8 indexed citations
15.
Wu, Jie & Jian Luan. (2020). Adversarially Trained Multi-Singer Sequence-to-Sequence Singing Synthesizer. 1296–1300. 13 indexed citations
16.
Wu, Jie, et al.. (2019). Vocal Pitch Extraction in Polyphonic Music Using Convolutional Residual Network. 2010–2014. 6 indexed citations
18.
Jian, Li, et al.. (2009). The Toshiba Mandarin TTS System for the Blizzard Challenge 2009. 95–100. 1 indexed citations
19.
Li, Jian, et al.. (2008). The Toshiba Mandarin TTS System for the Blizzard Challenge 2008. 94–99. 2 indexed citations
20.

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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