Shihab Shamma

17.1k total citations · 2 hit papers
205 papers, 11.1k citations indexed

About

Shihab Shamma is a scholar working on Cognitive Neuroscience, Signal Processing and Artificial Intelligence. According to data from OpenAlex, Shihab Shamma has authored 205 papers receiving a total of 11.1k indexed citations (citations by other indexed papers that have themselves been cited), including 155 papers in Cognitive Neuroscience, 71 papers in Signal Processing and 32 papers in Artificial Intelligence. Recurrent topics in Shihab Shamma's work include Neural dynamics and brain function (110 papers), Neuroscience and Music Perception (83 papers) and Hearing Loss and Rehabilitation (77 papers). Shihab Shamma is often cited by papers focused on Neural dynamics and brain function (110 papers), Neuroscience and Music Perception (83 papers) and Hearing Loss and Rehabilitation (77 papers). Shihab Shamma collaborates with scholars based in United States, France and United Kingdom. Shihab Shamma's co-authors include Jonathan B. Fritz, Mounya Elhilali, Stephen V. David, David J. Klein, Nima Mesgarani, Tai-Shih Chi, Didier A. Depireux, Jonathan Z. Simon, Powen Ru and Christophe Micheyl and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Neuron.

In The Last Decade

Shihab Shamma

195 papers receiving 10.7k citations

Hit Papers

Rapid task-related plasti... 2003 2026 2010 2018 2003 2014 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shihab Shamma United States 58 8.9k 3.1k 1.8k 1.3k 1.1k 205 11.1k
Christoph E. Schreiner United States 68 12.9k 1.4× 956 0.3× 1.7k 0.9× 3.5k 2.7× 506 0.5× 181 15.5k
Israel Nelken Israel 49 7.8k 0.9× 606 0.2× 1.7k 0.9× 1.5k 1.1× 315 0.3× 130 9.2k
Terence W. Picton Canada 76 22.2k 2.5× 1.8k 0.6× 5.0k 2.8× 4.8k 3.7× 319 0.3× 173 24.9k
Josh H. McDermott United States 38 9.2k 1.0× 1.3k 0.4× 2.7k 1.5× 384 0.3× 290 0.3× 97 10.4k
John C. Middlebrooks United States 43 5.7k 0.6× 1.3k 0.4× 1.5k 0.8× 2.0k 1.5× 103 0.1× 95 6.5k
Barbara Shinn‐Cunningham United States 47 6.8k 0.8× 1.5k 0.5× 2.1k 1.2× 1.7k 1.3× 156 0.1× 252 7.6k
Roy D. Patterson United Kingdom 43 5.8k 0.6× 2.1k 0.7× 1.7k 0.9× 1.3k 1.0× 545 0.5× 185 7.2k
Josef P. Rauschecker United States 66 15.0k 1.7× 473 0.2× 5.8k 3.2× 2.8k 2.2× 311 0.3× 166 17.4k
Robert P. Carlyon United Kingdom 50 7.2k 0.8× 1.8k 0.6× 1.5k 0.8× 2.4k 1.8× 118 0.1× 246 7.9k
Fan‐Gang Zeng United States 51 9.9k 1.1× 3.8k 1.2× 1.7k 0.9× 4.3k 3.3× 230 0.2× 201 11.2k

Countries citing papers authored by Shihab Shamma

Since Specialization
Citations

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

Fields of papers citing papers by Shihab Shamma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shihab Shamma

This figure shows the co-authorship network connecting the top 25 collaborators of Shihab Shamma. A scholar is included among the top collaborators of Shihab Shamma 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 Shihab Shamma. Shihab Shamma 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
1.
Gao, Fei, et al.. (2024). IDyOMpy: A new Python-based model for the statistical analysis of musical expectations. Journal of Neuroscience Methods. 415. 110347–110347.
2.
Englitz, Bernhard, Sahar Akram, Mounya Elhilali, & Shihab Shamma. (2024). Decoding contextual influences on auditory perception from primary auditory cortex. eLife. 13. 1 indexed citations
3.
Johns, Michael, et al.. (2023). Performance on stochastic figure-ground perception varies with individual differences in speech-in-noise recognition and working memory capacity. The Journal of the Acoustical Society of America. 153(1). 286–303. 2 indexed citations
4.
Shamma, Shihab, et al.. (2021). Binding the Acoustic Features of an Auditory Source through Temporal Coherence. Cerebral Cortex Communications. 2(4). tgab060–tgab060. 3 indexed citations
5.
Ponsot, Emmanuel, et al.. (2021). Mechanisms of Spectrotemporal Modulation Detection for Normal- and Hearing-Impaired Listeners. Trends in Hearing. 25. 2761989741–2761989741. 5 indexed citations
6.
Demany, Laurent, et al.. (2021). The perception of octave pitch affinity and harmonic fusion have a common origin. Hearing Research. 404. 108213–108213. 10 indexed citations
7.
Liberto, Giovanni M. Di, Roberta Bianco, Ashesh D. Mehta, et al.. (2020). Cortical encoding of melodic expectations in human temporal cortex. eLife. 9. 62 indexed citations
8.
Verschooten, Eric, Shihab Shamma, Andrew J. Oxenham, et al.. (2019). The upper frequency limit for the use of phase locking to code temporal fine structure in humans: A compilation of viewpoints. Hearing Research. 377. 109–121. 73 indexed citations
9.
Lu, Kai, et al.. (2019). Adaptive Efficient Coding of Correlated Acoustic Properties. Journal of Neuroscience. 39(44). 8664–8678. 7 indexed citations
10.
Elgueda, Diego, Daniel Duque, Susanne Radtke‐Schuller, et al.. (2019). State-dependent encoding of sound and behavioral meaning in a tertiary region of the ferret auditory cortex. Nature Neuroscience. 22(3). 447–459. 43 indexed citations
11.
Sheikhattar, Alireza, Ji Liu, Jonathan B. Fritz, et al.. (2018). Extracting neuronal functional network dynamics via adaptive Granger causality analysis. Proceedings of the National Academy of Sciences. 115(17). E3869–E3878. 58 indexed citations
12.
Mesgarani, Nima, Stephen V. David, Jonathan B. Fritz, & Shihab Shamma. (2014). Mechanisms of noise robust representation of speech in primary auditory cortex. Proceedings of the National Academy of Sciences. 111(18). 6792–6797. 94 indexed citations
13.
Torben-Nielsen, Benjamin, et al.. (2014). Spatially Distributed Dendritic Resonance Selectively Filters Synaptic Input. PLoS Computational Biology. 10(8). e1003775–e1003775. 16 indexed citations
14.
Nelken, Israel, et al.. (2014). Auditory Cortical Processing in Real-World Listening: The Auditory System Going Real. Journal of Neuroscience. 34(46). 15135–15138. 16 indexed citations
15.
David, Stephen V., et al.. (2014). Emergent Selectivity for Task-Relevant Stimuli in Higher-Order Auditory Cortex. Neuron. 82(2). 486–499. 99 indexed citations
16.
Zhou, Xinhui, Daniel Garcia-Romero, Ramani Duraiswami, Carol Espy-Wilson, & Shihab Shamma. (2011). Linear versus mel frequency cepstral coefficients for speaker recognition. 559–564. 105 indexed citations
17.
Mesgarani, Nima, Shihab Shamma, Ken W. Grant, & Ramani Duraiswami. (2003). Augmented intelligibility in simultaneous multi-talker environments. SMARTech Repository (Georgia Institute of Technology). 6(3). 295–9. 6 indexed citations
18.
Duraiswami, Ramani, Richard O. Duda, Laura Davis, et al.. (2000). Creating Virtual Spatial Audio Via Scientific Computing and Computer Vision. eScholarship (California Digital Library). 6 indexed citations
19.
Shamma, Shihab, et al.. (1990). A Functional Model of Primary Auditory Cortex: Spectral Orientation Columns. Digital Repository at the University of Maryland (University of Maryland College Park). 16–9. 4 indexed citations
20.
Shamma, Shihab. (1989). Spatial and temporal processing in central auditory networks. MIT Press eBooks. 247–289. 31 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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026