World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
80
Citations
33506
World Ranking
1060
National Ranking
567

Electronics and Electrical Engineering

D-Index
75
Citations
29149
World Ranking
665
National Ranking
296

Research.com Recognitions

  • 2018 - IEEE Fellow For contributions to speech, audio, and music processing

Overview

Daniel P. W. Ellis is affiliated with Google in the United States and specializes in research within the field of Computer Science. Their work primarily focuses on Signal Processing, with additional contributions in Infectious Diseases, Computer Vision and Pattern Recognition, Speech and Hearing, and Music.

Their research covers several main topics including Music and Audio Processing, Speech and Audio Processing, Music Technology and Sound Studies, Noise Effects and Management, Diverse Musicological Studies, Hearing Loss and Rehabilitation, and Acoustic Wave Phenomena Research.

Daniel P. W. Ellis has published extensively in venues such as:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • IEEE Signal Processing Letters
  • Journal of Occupational and Environmental Hygiene

Recent papers include:

  • MuLan: A Joint Embedding of Music Audio and Natural Language, 2022, arXiv (Cornell University)
  • Into the Wild with AudioScope: Unsupervised Audio-Visual Separation of On-Screen Sounds, 2020, arXiv (Cornell University)
  • Addressing Missing Labels in Large-Scale Sound Event Recognition Using a Teacher-Student Framework With Loss Masking, 2020, IEEE Signal Processing Letters
  • Proceedings of the 6th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2021), 2021, arXiv (Cornell University)
  • Description and analysis of novelties introduced in DCASE Task 4 2022 on the baseline system, 2022, arXiv (Cornell University)

Frequent co-authors in their research include:

  • Eduardo Fonseca
  • Aren Jansen
  • Manoj Plakal
  • Shawn Hershey
  • Frederic Font

Daniel P. W. Ellis was recognized as an IEEE Fellow in 2018 for contributions to speech, audio, and music processing.

Best Publications

  • librosa: Audio and Music Signal Analysis in Python

    Brian McFee;Colin Raffel;Dawen Liang;Daniel P.W. Ellis

  • Audio Set: An ontology and human-labeled dataset for audio events

    Jort F. Gemmeke;Daniel P. W. Ellis;Dylan Freedman;Aren Jansen

  • CNN architectures for large-scale audio classification

    Shawn Hershey;Sourish Chaudhuri;Daniel P. W. Ellis;Jort F. Gemmeke

  • THE MILLION SONG DATASET

    Thierry Bertin-Mahieux;Daniel P. W. Ellis;Brian Whitman;Paul Lamere

  • Speech and Audio Signal Processing: Processing and Perception of Speech and Music

    Ben Gold;Nelson Morgan;Dan Ellis

  • Tandem connectionist feature extraction for conventional HMM systems

    H. Hermansky;D.P.W. Ellis;S. Sharma

  • The ICSI Meeting Corpus

    A. Janin;D. Baron;J. Edwards;D. Ellis

  • Beat Tracking by Dynamic Programming

    Daniel P. W. Ellis

  • Identifying `Cover Songs' with Chroma Features and Dynamic Programming Beat Tracking

    D. P. W. Ellis;G. E. Poliner

  • Prediction-driven computational auditory scene analysis

    Daniel P. W. Ellis;Barry L. Vercoe

  • A Large-Scale Evaluation of Acoustic and Subjective Music-Similarity Measures

    Adam Berenzweig;Beth Logan;Daniel P. W. Ellis;Brian P. W. Whitman;Brian P. W. Whitman

  • MIR_EVAL: A Transparent Implementation of Common MIR Metrics.

    Colin Raffel;Brian McFee;Eric J. Humphrey;Justin Salamon

  • Chord Segmentation and Recognition using EM-Trained Hidden Markov Models

    Alexander Sheh;Daniel P. W. Ellis

  • Song-Level Features and Support Vector Machines for Music Classification

    Michael I. Mandel;Daniel P. W. Ellis

  • Consumer video understanding: a benchmark database and an evaluation of human and machine performance

    Yu-Gang Jiang;Guangnan Ye;Shih-Fu Chang;Daniel Ellis

  • Signal Processing for Music Analysis

    M. Muller;D. P. W. Ellis;A. Klapuri;G. Richard

  • Model-Based Expectation-Maximization Source Separation and Localization

    M.I. Mandel;R.J. Weiss;D. Ellis

  • A discriminative model for polyphonic piano transcription

    Graham E. Poliner;Daniel P. W. Ellis

  • Melody Extraction from Polyphonic Music Signals: Approaches, applications, and challenges

    Justin Salamon;Emilia Gomez;Daniel P. W. Ellis;Gael Richard

  • Melody Transcription From Music Audio: Approaches and Evaluation

    G.E. Poliner;D.P.W. Ellis;A.F. Ehmann;E. Gomez

  • Speech and Audio Signal Processing

    Simon King;Dan Ellis;Nelson Morgan

  • 8. Pattern Classification

    Unknown

Frequent Co-Authors

Nelson Morgan
Nelson Morgan International Computer Science Institute
Aren Jansen
Aren Jansen Google (United States)
Shih-Fu Chang
Shih-Fu Chang Columbia University
Hynek Hermansky
Hynek Hermansky Johns Hopkins University
Colin Raffel
Colin Raffel University of Toronto
Xavier Serra
Xavier Serra Pompeu Fabra University
Alexander C. Loui
Alexander C. Loui Rochester Institute of Technology
Martin Cooke
Martin Cooke Ikerbasque
Malcolm Slaney
Malcolm Slaney Stanford University

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