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Computer Science

D-Index
45
Citations
12706
World Ranking
7038
National Ranking
3088

Overview

Martin J. Strauss is a researcher affiliated with the University of Michigan-Ann Arbor in the United States. Their work primarily spans the domain of computer science, with a notable focus on signal processing and related subfields.

The researcher has contributed to areas encompassing speech and audio processing, music and audio processing, and hearing loss and rehabilitation. Their expertise also extends to advanced adaptive filtering techniques, ethics and social impacts of artificial intelligence, auction theory and applications, and privacy-preserving technologies in data.

Their recent scholarly output includes publications in various venues, most prominently arXiv (Cornell University) and the Leibniz-Zentrum für Informatik (Schloss Dagstuhl). The recent papers authored or co-authored by Martin J. Strauss include:

  • A Hands-on Comparison of DNNs for Dialog Separation Using Transfer Learning from Music Source Separation, 2021, arXiv (Cornell University)
  • Improved Normalizing Flow-Based Speech Enhancement using an All-pole Gammatone Filterbank for Conditional Input Representation, 2022, arXiv (Cornell University)
  • Predicting Preferred Dialogue-to-Background Loudness Difference in Dialogue-Separated Audio, 2023, arXiv (Cornell University)
  • FlowMAC: Conditional Flow Matching for Audio Coding at Low Bit Rates, 2024, arXiv (Cornell University)
  • Multiplicative Metric Fairness Under Composition, 2023, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)

Martin J. Strauss frequently collaborates with several co-authors, including Bernd Edler, Matteo Torcoli, Amanda Bower, Sarah N. Kitchen, and Laura Niss. Among these, Bernd Edler and Matteo Torcoli are the most recurrent co-authors.

Their primary research encompasses the following fields of study:

  • Computer Science

Subfields of study include:

  • Signal Processing
  • Artificial Intelligence
  • Cognitive Neuroscience
  • Computational Mechanics
  • Safety Research

Key topics of their work are:

  • Speech and Audio Processing
  • Music and Audio Processing
  • Hearing Loss and Rehabilitation
  • Advanced Adaptive Filtering Techniques
  • Ethics and Social Impacts of AI
  • Auction Theory and Applications
  • Privacy-Preserving Technologies in Data

Best Publications

  • Divertible protocols and atomic proxy cryptography

    Matt Blaze;Gerrit Bleumer;Martin Strauss

  • Algorithms for simultaneous sparse approximation: part I: Greedy pursuit

    Joel A. Tropp;Anna C. Gilbert;Martin J. Strauss

  • Surfing Wavelets on Streams: One-Pass Summaries for Approximate Aggregate Queries

    Anna C. Gilbert;Yannis Kotidis;S. Muthukrishnan;Martin Strauss

  • Referee: trust management for Web applications

    Yang-Hua Chu;Joan Feigenbaum;Brian LaMacchia;Paul Resnick

  • Combining geometry and combinatorics: A unified approach to sparse signal recovery

    R. Berinde;A.C. Gilbert;P. Indyk;H. Karloff

  • Compliance Checking in the PolicyMaker Trust Management System

    Matt Blaze;Joan Feigenbaum;Martin Strauss

  • Simultaneous sparse approximation via greedy pursuit

    J.A. Tropp;A.C. Gilbert;M.J. Strauss

  • Near-optimal sparse fourier representations via sampling

    A. C. Gilbert;S. Guha;P. Indyk;S. Muthukrishnan

  • Fast, small-space algorithms for approximate histogram maintenance

    Anna C. Gilbert;Sudipto Guha;Piotr Indyk;Yannis Kotidis

  • One sketch for all: fast algorithms for compressed sensing

    A. C. Gilbert;M. J. Strauss;J. A. Tropp;R. Vershynin

  • An Approximate L 1 -Difference Algorithm for Massive Data Streams

    Joan Feigenbaum;Sampath Kannan;Martin J. Strauss;Mahesh Viswanathan

  • Random Sampling for Analog-to-Information Conversion of Wideband Signals

    J. Laska;S. Kirolos;Y. Massoud;R. Baraniuk

  • Maintaining time-decaying stream aggregates

    Edith Cohen;Martin Strauss

  • Improved time bounds for near-optimal sparse Fourier representations

    A. C. Gilbert;S. Muthukrishnan;M. Strauss

  • An approximate L/sup 1/-difference algorithm for massive data streams

    J. Feigenbaum;S. Kannan;M. Strauss;M. Viswanathan

  • Approximation of functions over redundant dictionaries using coherence

    Anna C. Gilbert;S. Muthukrishnan;Martin J. Strauss

  • How to summarize the universe: dynamic maintenance of quantiles

    Anna C. Gilbert;Yannis Kotidis;S. Muthukrishnan;Martin J. Strauss

  • Networks of strong ties

    Xiaolin Shi;Lada A. Adamic;Martin J. Strauss

  • One-pass wavelet decompositions of data streams

    A.C. Gilbert;Y. Kotidis;S. Muthukrishnan;M.J. Strauss

  • Algorithmic linear dimension reduction in the l_1 norm for sparse vectors

    Anna C. Gilbert;Martin J. Strauss;Joel A. Tropp;Roman Vershynin

  • An approximate Lp-difference algorithm for massive data streams

    J. H. Fong;M. J. Strauss

Frequent Co-Authors

Anna C. Gilbert
Anna C. Gilbert Yale University
Joan Feigenbaum
Joan Feigenbaum Yale University
s muthukrishnan
s muthukrishnan Rutgers, The State University of New Jersey
Ely Porat
Ely Porat Bar-Ilan University
Joel A. Tropp
Joel A. Tropp California Institute of Technology
Rebecca N. Wright
Rebecca N. Wright Barnard College
Sampath Kannan
Sampath Kannan University of Pennsylvania
Yuval Ishai
Yuval Ishai Technion – Israel Institute of Technology
Richard G. Baraniuk
Richard G. Baraniuk Rice University

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