Malcolm Slaney

19.6k total citations · 7 hit papers
133 papers, 12.4k citations indexed

About

Malcolm Slaney is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Malcolm Slaney has authored 133 papers receiving a total of 12.4k indexed citations (citations by other indexed papers that have themselves been cited), including 83 papers in Signal Processing, 59 papers in Computer Vision and Pattern Recognition and 35 papers in Artificial Intelligence. Recurrent topics in Malcolm Slaney's work include Speech and Audio Processing (61 papers), Music and Audio Processing (51 papers) and Hearing Loss and Rehabilitation (24 papers). Malcolm Slaney is often cited by papers focused on Speech and Audio Processing (61 papers), Music and Audio Processing (51 papers) and Hearing Loss and Rehabilitation (24 papers). Malcolm Slaney collaborates with scholars based in United States, United Kingdom and Denmark. Malcolm Slaney's co-authors include Avinash C. Kak, Ge Wang, Eric D. Scheirer, Michael A. Casey, Michele Covell, A.C. Kak, Christoph Bregler, L.E. Larsen, Shihab Shamma and Nima Mesgarani and has published in prestigious journals such as Journal of Neuroscience, SHILAP Revista de lepidopterología and NeuroImage.

In The Last Decade

Malcolm Slaney

130 papers receiving 11.5k citations

Hit Papers

Principles of Computerize... 1984 2026 1998 2012 2001 2017 2002 2002 2014 1000 2.0k 3.0k

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Malcolm Slaney 4.2k 3.6k 2.8k 2.4k 1.8k 133 12.4k
Michaël Unser 3.3k 0.8× 15.7k 4.3× 5.6k 2.0× 5.3k 2.2× 1.9k 1.0× 690 35.2k
Aggelos K. Katsaggelos 3.2k 0.8× 11.6k 3.2× 1.7k 0.6× 1.1k 0.4× 1.6k 0.9× 819 18.1k
Alan V. Oppenheim 5.7k 1.4× 4.5k 1.3× 2.1k 0.8× 577 0.2× 2.4k 1.4× 174 18.4k
Richard M. Leahy 2.2k 0.5× 2.6k 0.7× 3.2k 1.1× 8.2k 3.4× 854 0.5× 387 20.2k
Ronald W. Schafer 5.2k 1.2× 5.7k 1.6× 2.4k 0.8× 634 0.3× 2.2k 1.2× 203 18.6k
Chris Taylor 2.1k 0.5× 14.2k 3.9× 3.0k 1.1× 3.2k 1.3× 2.2k 1.2× 639 26.3k
Jun-Yan Zhu 1.1k 0.3× 15.0k 4.1× 1.8k 0.6× 2.0k 0.8× 4.9k 2.7× 71 21.2k
Berthold K. P. Horn 904 0.2× 18.9k 5.2× 2.0k 0.7× 1.6k 0.7× 1.5k 0.8× 157 27.3k
Hamid R. Sheikh 3.3k 0.8× 35.5k 9.8× 4.1k 1.4× 4.0k 1.7× 2.7k 1.5× 26 46.1k
Karen Egiazarian 1.2k 0.3× 13.5k 3.7× 2.1k 0.7× 1.2k 0.5× 677 0.4× 471 17.8k

Countries citing papers authored by Malcolm Slaney

Since Specialization
Citations

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

Fields of papers citing papers by Malcolm Slaney

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Malcolm Slaney

This figure shows the co-authorship network connecting the top 25 collaborators of Malcolm Slaney. A scholar is included among the top collaborators of Malcolm Slaney 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 Malcolm Slaney. Malcolm Slaney 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.
Hjortkjær, Jens, Daniel D.E. Wong, Enea Ceolini, et al.. (2025). Real-time control of a hearing instrument with EEG-based attention decoding. Journal of Neural Engineering. 22(1). 16027–16027. 4 indexed citations
2.
Slaney, Malcolm & Matthew B. Fitzgerald. (2024). Comparing human and machine speech recognition in noise with QuickSIN. SHILAP Revista de lepidopterología. 4(9). 4 indexed citations
3.
Misiunas, Karolis, et al.. (2023). Neural architecture search for energy-efficient always-on audio machine learning. Neural Computing and Applications. 35(16). 12133–12144. 13 indexed citations
4.
Cheveigné, Alain de, Malcolm Slaney, Søren A. Fuglsang, & Jens Hjortkjær. (2021). Auditory stimulus-response modeling with a match-mismatch task. Journal of Neural Engineering. 18(4). 46040–46040. 20 indexed citations
5.
Zhao, Sijia, Lucas Benjamin, Elia Benhamou, et al.. (2019). Rapid Ocular Responses Are Modulated by Bottom-up-Driven Auditory Salience. Journal of Neuroscience. 39(39). 7703–7714. 27 indexed citations
6.
Slaney, Malcolm, et al.. (2018). Connecting Deep Neural Networks to Physical, Perceptual, and Electrophysiological Auditory Signals. Frontiers in Neuroscience. 12. 532–532. 11 indexed citations
7.
Cheveigné, Alain de, Daniel D.E. Wong, Giovanni M. Di Liberto, et al.. (2018). Decoding the auditory brain with canonical component analysis. NeuroImage. 172. 206–216. 127 indexed citations
8.
Wong, Daniel D.E., Søren A. Fuglsang, Jens Hjortkjær, et al.. (2018). A Comparison of Regularization Methods in Forward and Backward Models for Auditory Attention Decoding. Frontiers in Neuroscience. 12. 531–531. 87 indexed citations
9.
Hershey, Shawn, Sourish Chaudhuri, Daniel P. W. Ellis, et al.. (2017). CNN architectures for large-scale audio classification. 131–135. 1432 indexed citations breakdown →
10.
O’Sullivan, James, Alan J. Power, Nima Mesgarani, et al.. (2014). Attentional Selection in a Cocktail Party Environment Can Be Decoded from Single-Trial EEG. Cerebral Cortex. 25(7). 1697–1706. 531 indexed citations breakdown →
11.
Marlin, Benjamin M., Richard S. Zemel, Sam T. Roweis, & Malcolm Slaney. (2011). Recommender systems: missing data and statistical model estimation. International Joint Conference on Artificial Intelligence. 2686–2691. 23 indexed citations
12.
Yu, Theodore, et al.. (2009). Periodicity detection and localization using spike timing from the AER EAR. Zurich Open Repository and Archive (University of Zurich). 109–112. 8 indexed citations
13.
Slaney, Malcolm, et al.. (2006). A statistical model of timbre perception.. Conference of the International Speech Communication Association. 18–23. 3 indexed citations
14.
Pitts, Marian, et al.. (2006). A randomised controlled trial comparing computer-assisted with face-to-face sexual history taking in a clinical setting. Sexually Transmitted Infections. 83(1). 52–56. 53 indexed citations
15.
Russell, Daniel M. & Malcolm Slaney. (2004). Measuring the Tools and Behaviors of Sensemaking. Biochimica et Biophysica Acta. 980(1). 1–8. 1 indexed citations
16.
Slaney, Malcolm & Michele Covell. (2000). FaceSync: A Linear Operator for Measuring Synchronization of Video Facial Images and Audio Tracks. Neural Information Processing Systems. 13. 814–820. 100 indexed citations
17.
Slaney, Malcolm. (1998). A critique of pure audition. CRC Press eBooks. 27–41. 26 indexed citations
18.
Bregler, Christoph, Michele Covell, & Malcolm Slaney. (1997). Video rewrite: visual speech synthesis from video.. AVSP. 153–156. 11 indexed citations
19.
Slaney, Malcolm. (1994). Pattern Playback in the 90s. Neural Information Processing Systems. 7. 827–834. 2 indexed citations
20.
Slaney, Malcolm & Richard F. Lyon. (1993). On the importance of time—a temporal representation of sound. John Wiley & Sons, Inc. eBooks. 95–116. 90 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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