World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
65
Citations
15118
World Ranking
2479
National Ranking
141

Electronics and Electrical Engineering

D-Index
59
Citations
12980
World Ranking
1762
National Ranking
85

Research.com Recognitions

  • 2015 - IEEE Fellow For contributions to latent variable analysis

Overview

Mark D. Plumbley is affiliated with the University of Surrey in the United Kingdom. Their research primarily focuses on fields related to computer science, with a particular concentration on signal processing and artificial intelligence. Their work spans several subfields including signal processing, artificial intelligence, computer vision and pattern recognition, developmental biology, and biomedical engineering.

Their research addresses topics centered on music and audio processing, speech and audio processing, speech recognition and synthesis, music technology and sound studies, natural language processing techniques, video analysis and summarization, and animal vocal communication and behavior.

Mark D. Plumbley has co-authored many publications with frequent collaborators such as Wenwu Wang, Haohe Liu, Xubo Liu, Qiuqiang Kong, and Xinhao Mei. These co-authors have contributed alongside Plumbley to a significant number of works in audio-related fields.

The scientist has published extensively in notable venues, including:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • IEEE/ACM Transactions on Audio Speech and Language Processing
  • IEEE Transactions on Audio Speech and Language Processing
  • NOISE-CON proceedings

Selected recent works include:

  • "PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition," 2020, IEEE/ACM Transactions on Audio Speech and Language Processing
  • "Sound Event Detection: A tutorial," 2021, IEEE Signal Processing Magazine
  • "Sound Event Detection of Weakly Labelled Data With CNN-Transformer and Automatic Threshold Optimization," 2020, IEEE/ACM Transactions on Audio Speech and Language Processing
  • "AudioLDM 2: Learning Holistic Audio Generation With Self-Supervised Pretraining," 2024, IEEE/ACM Transactions on Audio Speech and Language Processing
  • "WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research," 2024, IEEE/ACM Transactions on Audio Speech and Language Processing

Mark D. Plumbley was recognized as an IEEE Fellow in 2015 for contributions to latent variable analysis.

Best Publications

  • PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition

    Qiuqiang Kong;Yin Cao;Turab Iqbal;Yuxuan Wang

  • Best Practices for Scientific Computing

    Greg Wilson;D. A. Aruliah;C. Titus Brown;Neil P. Chue Hong

  • Detection and Classification of Acoustic Scenes and Events

    Dan Stowell;Dimitrios Giannoulis;Emmanouil Benetos;Mathieu Lagrange

  • Acoustic Scene Classification: Classifying environments from the sounds they produce

    Daniele Barchiesi;Dimitrios Giannoulis;Dan Stowell;Mark D. Plumbley

  • Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge

    Annamaria Mesaros;Toni Heittola;Emmanouil Benetos;Peter Foster

  • Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning

    Dan Stowell;Mark D. Plumbley

  • Context-Dependent Beat Tracking of Musical Audio

    M.E.P. Davies;M.D. Plumbley

  • Algorithms for nonnegative independent component analysis

    M.D. Plumbley

  • Sparse Representations in Audio and Music: From Coding to Source Separation

    Mark D Plumbley;Thomas Blumensath;Laurent Daudet;Remi Gribonval

  • Audio Inpainting

    A. Adler;V. Emiya;M. G. Jafari;M. Elad

  • Computational Analysis of Sound Scenes and Events

    Tuomas Virtanen;Mark D. Plumbley;Dan Ellis

  • Detection and classification of acoustic scenes and events: An IEEE AASP challenge

    Dimitrios Giannoulis;Emmanouil Benetos;Dan Stowell;Mathias Rossignol

  • Large-Scale Weakly Supervised Audio Classification Using Gated Convolutional Neural Network

    Yong Xu;Qiuqiang Kong;Wenwu Wang;Mark D. Plumbley

  • Sound Event Detection: A tutorial

    Annamaria Mesaros;Toni Heittola;Tuomas Virtanen;Mark D. Plumbley

  • Theorems on Positive Data: on the Uniqueness of NMF

    Hans Laurberg;Mads Græsbøll Christensen;Mark D. Plumbley;Lars Kai Hansen

  • Wideband Spectrum Sensing on Real-Time Signals at Sub-Nyquist Sampling Rates in Single and Cooperative Multiple Nodes

    Zhijin Qin;Yue Gao;Mark D. Plumbley;Clive G. Parini

  • Fast Dictionary Learning for Sparse Representations of Speech Signals

    Maria G. Jafari;Mark D. Plumbley

  • Acoustic Scene Classification

    Daniele Barchiesi;Dimitrios Giannoulis;Dan Stowell;Mark D. Plumbley

  • A "nonnegative PCA" algorithm for independent component analysis

    M.D. Plumbley;E. Oja

  • Automatic music transcription and audio source separation

    Mark D. Plumbley;Samer A. Abdallah;Juan Pablo Bello;Mike E. Davies

  • SparseRepresentationsinAudio and Music: From Coding to Source Separation The fidelity of music and other audio can usually be accurately and rapidly predicted from a relatively small sample of signal information.

    Mark D. Plumbley;Thomas Blumensath;Laurent Daudet;Remi Gribonval

Frequent Co-Authors

Wenwu Wang
Wenwu Wang University of Surrey
Emmanuel Vincent
Emmanuel Vincent University of Lorraine
Mark Sandler
Mark Sandler Google (United States)
Juan Pablo Bello
Juan Pablo Bello New York University
Rémi Gribonval
Rémi Gribonval École Normale Supérieure de Lyon
Michael Elad
Michael Elad Technion – Israel Institute of Technology
Anssi Klapuri
Anssi Klapuri Yousician
Björn Schuller
Björn Schuller Imperial College London
Tuomas Virtanen
Tuomas Virtanen Tampere University
Yuxuan Wang
Yuxuan Wang ByteDance

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