Shubo Lv

992 total citations · 1 hit paper
9 papers, 597 citations indexed

About

Shubo Lv is a scholar working on Signal Processing, Artificial Intelligence and Mechanics of Materials. According to data from OpenAlex, Shubo Lv has authored 9 papers receiving a total of 597 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Signal Processing, 8 papers in Artificial Intelligence and 1 paper in Mechanics of Materials. Recurrent topics in Shubo Lv's work include Speech and Audio Processing (9 papers), Speech Recognition and Synthesis (8 papers) and Music and Audio Processing (5 papers). Shubo Lv is often cited by papers focused on Speech and Audio Processing (9 papers), Speech Recognition and Synthesis (8 papers) and Music and Audio Processing (5 papers). Shubo Lv collaborates with scholars based in China. Shubo Lv's co-authors include Lei Xie, Yanxin Hu, Shimin Zhang, Yihui Fu, Mengtao Xing, Yun Liu, Jian Wu, Yannan Wang, Yun Liu and Jingdong 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

Shubo Lv

9 papers receiving 583 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement

2020 432 citations Yanxin Hu, Yun Liu et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shubo Lv China	5	552	386	221	85	45	9	597
Yihui Fu China	7	565 1.0×	420 1.1×	207 0.9×	78 0.9×	46 1.0×	12	626
Mengtao Xing China	2	430 0.8×	292 0.8×	173 0.8×	70 0.8×	37 0.8×	2	467
Lianwu Chen China	15	511 0.9×	312 0.8×	178 0.8×	72 0.8×	27 0.6×	31	554
Yan-Hui Tu China	12	414 0.8×	270 0.7×	121 0.5×	45 0.5×	26 0.6×	29	497
Volker Leutnant Germany	6	490 0.9×	308 0.8×	163 0.7×	85 1.0×	34 0.8×	16	518
Aditya Arie Nugraha Japan	10	617 1.1×	301 0.8×	206 0.9×	46 0.5×	44 1.0×	31	676
Armin Sehr Germany	8	742 1.3×	450 1.2×	261 1.2×	132 1.6×	40 0.9×	24	781
Buye Xu United States	10	346 0.6×	183 0.5×	148 0.7×	108 1.3×	20 0.4×	49	414
Tsubasa Ochiai Japan	16	784 1.4×	667 1.7×	96 0.4×	44 0.5×	39 0.9×	58	854
Guoning Hu United States	10	537 1.0×	208 0.5×	178 0.8×	164 1.9×	40 0.9×	19	570

Countries citing papers authored by Shubo Lv

Since Specialization

Citations

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

Fields of papers citing papers by Shubo Lv

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shubo Lv

This figure shows the co-authorship network connecting the top 25 collaborators of Shubo Lv. A scholar is included among the top collaborators of Shubo Lv 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 Shubo Lv. Shubo Lv 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:

9 of 9 papers shown

1.

Lv, Shubo, et al.. (2023). Two-Stage Neural Network for ICASSP 2023 Speech Signal Improvement Challenge. 4 indexed citations

2.

Lv, Shubo, et al.. (2023). Spatial-DCCRN: DCCRN Equipped with Frame-Level Angle Feature and Hybrid Filtering for Multi-Channel Speech Enhancement. 436–443. 4 indexed citations

3.

Lv, Shubo, et al.. (2023). DCCRN-KWS: An Audio Bias Based Model for Noise Robust Small-Footprint Keyword Spotting. 929–933. 1 indexed citations

4.

Lv, Shubo, et al.. (2023). MBTFNET: Multi-Band Temporal-Frequency Neural Network for Singing Voice Enhancement. 1–8. 1 indexed citations

5.

Rao, Wei, Yihui Fu, Shubo Lv, et al.. (2022). TEA-PSE: Tencent-Ethereal-Audio-Lab Personalized Speech Enhancement System for ICASSP 2022 DNS Challenge. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 9291–9295. 27 indexed citations

6.

Lv, Shubo, Yihui Fu, Mengtao Xing, et al.. (2022). S-DCCRN: Super Wide Band DCCRN with Learnable Complex Feature for Speech Enhancement. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 7767–7771. 35 indexed citations

7.

Fu, Yihui, et al.. (2022). Uformer: A Unet Based Dilated Complex & Real Dual-Path Conformer Network for Simultaneous Speech Enhancement and Dereverberation. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 7417–7421. 36 indexed citations

8.

Lv, Shubo, Yanxin Hu, Shimin Zhang, & Lei Xie. (2021). DCCRN+: Channel-Wise Subband DCCRN with SNR Estimation for Speech Enhancement. 2816–2820. 57 indexed citations

9.

Hu, Yanxin, Yun Liu, Shubo Lv, et al.. (2020). DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement. 2472–2476. 432 indexed citations breakdown →

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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