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Jesper Jensen

Jesper Jensen

D-Index & Metrics

Computer Science

D-Index
43
Citations
12486
World Ranking
7788
National Ranking
33

Overview

Jesper Jensen is affiliated with Aalborg University in Denmark and has contributed extensively to research in the fields of Computer Science and Engineering. Their primary research interests lie within Signal Processing, with significant focus on Speech and Audio Processing and Advanced Adaptive Filtering Techniques.

Their work spans several specialized subfields, including Computational Mechanics, Electrical and Electronic Engineering, Artificial Intelligence, and Aerospace Engineering. Jesper Jensen's research topics include:

  • Speech and Audio Processing
  • Advanced Adaptive Filtering Techniques
  • Indoor and Outdoor Localization Technologies
  • Blind Source Separation Techniques
  • Speech Recognition and Synthesis
  • Robotics and Sensor-Based Localization
  • Underwater Acoustics Research

Jesper Jensen has published frequently in various academic venues. Noteworthy publication venues include:

  • arXiv (Cornell University)
  • EURASIP Journal on Audio Speech and Music Processing
  • Signal Processing
  • Applied Acoustics
  • IEEE Transactions on Audio Speech and Language Processing

Recent publications reflect contributions mainly to audio and acoustic signal processing. Selected papers include:

  • "Acoustic DOA estimation using space alternating sparse Bayesian learning," 2021, EURASIP Journal on Audio Speech and Music Processing
  • "A framework for spatial map generation using acoustic echoes for robotic platforms," 2022, Robotics and Autonomous Systems
  • "Estimation of acoustic echoes using expectation-maximization methods," 2020, EURASIP Journal on Audio Speech and Music Processing
  • "Experimental study of robust acoustic beamforming for speech acquisition in reverberant and noisy environments," 2020, Applied Acoustics
  • "Recursive least-squares algorithm based on a third-order tensor decomposition for low-rank system identification," 2023, Signal Processing

Jesper Jensen has collaborated frequently with several co-authors, including:

  • Mads Græsbøll Christensen
  • Usama Saqib
  • Sankha Subhra Bhattacharjee
  • Shuai Tao
  • Jacob Benesty

Their body of work emphasizes techniques in acoustic signal estimation, adaptive filtering algorithms, and spatial localization, applicable in various environments including robotics and noisy or reverberant spaces.

Best Publications

  • An Algorithm for Intelligibility Prediction of Time–Frequency Weighted Noisy Speech

    C. H. Taal;R. C. Hendriks;R. Heusdens;J. Jensen

  • A short-time objective intelligibility measure for time-frequency weighted noisy speech

    Cees H. Taal;Richard C. Hendriks;Richard Heusdens;Jesper Jensen

  • Permutation invariant training of deep models for speaker-independent multi-talker speech separation

    Dong Yu;Morten Kolbaek;Zheng-Hua Tan;Jesper Jensen

  • Multitalker Speech Separation With Utterance-Level Permutation Invariant Training of Deep Recurrent Neural Networks

    Morten Kolbaek;Dong Yu;Zheng-Hua Tan;Jesper Jensen

  • An Algorithm for Predicting the Intelligibility of Speech Masked by Modulated Noise Maskers

    Jesper Jensen;Cees H. Taal

  • MMSE based noise PSD tracking with low complexity

    Richard C. Hendriks;Richard Heusdens;Jesper Jensen

  • Minimum Mean-Square Error Estimation of Discrete Fourier Coefficients With Generalized Gamma Priors

    J.S. Erkelens;R.C. Hendriks;R.. Heusdens;J.. Jensen

  • An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation

    Daniel Michelsanti;Zheng-Hua Tan;Shi-Xiong Zhang;Yong Xu

  • Speech Intelligibility Potential of General and Specialized Deep Neural Network Based Speech Enhancement Systems

    Morten Kolbk;Zheng-Hua Tan;Jesper Jensen

  • Prediction of future fading based on past measurements

    J.B. Andersen;J. Jensen;S.H. Jensen;F. Frederiksen

  • DFT-Domain Based Single-Microphone Noise Reduction for Speech Enhancement: A Survey of the State of the Art

    Richard C. Hendriks;Timo Gerkmann;Jesper Jensen

  • Speech enhancement using a constrained iterative sinusoidal model

    J. Jensen;J.H.L. Hansen

  • On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement

    Morten Kolbaek;Zheng-Hua Tan;Soren Holdt Jensen;Jesper Jensen

  • Novel Acoustic Feedback Cancellation Approaches in Hearing Aid Applications Using Probe Noise and Probe Noise Enhancement

    Meng Guo;S. H. Jensen;J. Jensen

  • Noise Tracking Using DFT Domain Subspace Decompositions

    R.C. Hendriks;J. Jensen;R. Heusdens

  • Self-calibration of multi-microphone noise reduction system for hearing assistance devices using an auxiliary device

    Jesper Jensen;Michael Syskind Pedersen

  • On Optimal Linear Filtering of Speech for Near-End Listening Enhancement

    C. H. Taal;J. Jensen;A. Leijon

  • A perceptual model for sinusoidal audio coding based on spectral integration

    Steven van de Par;Armin Kohlrausch;Richard Heusdens;Jesper Jensen

  • A data-driven approach to optimizing spectral speech enhancement methods for various error criteria

    Jan Erkelens;Jesper Jensen;Richard Heusdens

  • An evaluation of objective measures for intelligibility prediction of time-frequency weighted noisy speech.

    Cees H. Taal;Richard C. Hendriks;Richard Heusdens;Jesper Jensen

  • Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks

    Morten Kolbæk;Dong Yu;Zheng-Hua Tan;Jesper Jensen

Frequent Co-Authors

Zheng-Hua Tan
Zheng-Hua Tan Aalborg University
Søren Holdt Jensen
Søren Holdt Jensen University of Extremadura
Jacob Benesty
Jacob Benesty Institut National de la Recherche Scientifique
Max A. Little
Max A. Little University of Birmingham
Dong Yu
Dong Yu Tencent (China)
Simon Doclo
Simon Doclo Carl von Ossietzky University of Oldenburg
Andreas Jakobsson
Andreas Jakobsson Lund University
Sharon Gannot
Sharon Gannot Bar-Ilan University
Jørgen Bach Andersen
Jørgen Bach Andersen Aalborg University

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