Mark D. Plumbley

发表

Gradient Polytope Faces Pursuit for large scale sparse recovery problems

Aris Gretsistas, Ivan Damnjanovic, Mark D. Plumbley, 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

Attention-based convolutional neural networks for acoustic scene classification

Mark D. Plumbley, Kun Qian, Zhao Ren, 2018, DCASE.

Harmonic Motion: A Toolkit for Processing Gestural Data for Interactive Sound

Mark D. Plumbley, Tim Murray Browne, 2014, NIME.

The Melody Triangle: Exploring Pattern and Predictability in Music

Mark D. Plumbley, Peter W. McOwan, Henrik Ekeus, 2012, MUME@AIIDE.

Eye Tracking as Interface for the Design of Generative Visual Forms and Patterns

Mark D. Plumbley, Peter W. McOwan, Henrik Ekeus, 2012 .

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

Dan Stowell, Mark D. Plumbley, Daniele Barchiesi, 2014, IEEE Signal Processing Magazine.

Best Practices for Scientific Computing

Ian M. Mitchell, Mark D. Plumbley, Neil P. Chue Hong, 2012, PLoS biology.

Beat tracking with a two state model [music applications]

Matthew E. P. Davies, Mark D. Plumbley, 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

A spectral difference approach to downbeat extraction in musical audio

Matthew E. P. Davies, Mark D. Plumbley, 2006, 2006 14th European Signal Processing Conference.

An Independent Component Analysis Approach to Automatic Music Transcription

Mark D. Plumbley, Samer A. Abdallah, S. Abdallah, 2003 .

Efficient compressive spectrum sensing algorithm for M2M devices

Yue Gao, Zhijin Qin, Laurie G. Cuthbert, 2014, 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

General-purpose audio tagging from noisy labels using convolutional neural networks

Mark D. Plumbley, Qiuqiang Kong, Wenwu Wang, 2018, DCASE.

Model-based audio source separation

Mark D. Plumbley, Mike E. Davies, Samer A. Abdallah, 2005 .

Structure-aware dictionary learning with harmonic atoms

Mark D. Plumbley, Ken O'Hanlon, 2011, 2011 19th European Signal Processing Conference.

Approximating Optimal Information Transmission using Local Hebbian Algorithms in a Double Feedback Loop

Mark D. Plumbley, 1993 .

Computational Analysis of Sound Scenes and Events

Mark D. Plumbley, Daniel P. W. Ellis, Tuomas Virtanen, 2017 .

Fast Iterative Shrinkage for Signal Declipping and Dequantization

Mark D. Plumbley, Wenwu Wang, Francis Bach, 2018, 1812.01540.

Sparse Recovery and Dictionary Learning From Nonlinear Compressive Measurements

Mark D. Plumbley, Wenwu Wang, Francis Bach, 2019, IEEE Transactions on Signal Processing.

Evaluation of audio source separation models using hypothesis-driven non-parametric statistical methods

Mark D. Plumbley, Antoine Liutkus, Andrew J. R. Simpson, 2016, 2016 24th European Signal Processing Conference (EUSIPCO).

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

Dan Stowell, Mathieu Lagrange, Mathias Rossignol, 2013, 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.

Two-Stage Single-Channel Audio Source Separation Using Deep Neural Networks

Mark D. Plumbley, Andrew J. R. Simpson, Emad M. Grais, 2017, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

Application of Geometric Dependency Analysis to the Separation of Convolved Mixtures

Mark D. Plumbley, Samer A. Abdallah, 2004, ICA.

The roles of neural and evolutionary computing in intelligent software systems

Mark D. Plumbley, B. Azvine, K. C. Tsui, 1996 .

Unsupervised neural network learning procedures for feature extraction and classification

Mark D. Plumbley, Suzanna Becker, S. Becker, 1996, Applied Intelligence.

DCASE 2018 Challenge baseline with convolutional neural networks pdf

Yong Xu, Mark D. Plumbley, Qiuqiang Kong, 2018, ArXiv.

Fully DNN-Based Multi-Label Regression for Audio Tagging pdf

Qiang Huang, Yong Xu, Mark D. Plumbley, 2016, DCASE.

A Hierarchical Latent Mixture Model for Polyphonic Music Analysis

Mark D. Plumbley, Cian O'Brien, Cian O'Brien, 2018, 2018 26th European Signal Processing Conference (EUSIPCO).

Object-Coding for Resolution-Free Musical Audio

Emmanuel Vincent, Mark D. Plumbley, Stephen J. Welburn, 2007 .

A comparison of two different methods for score-informed source separation

Mark D. Plumbley, Joachim Ganseman, Joachim Fritsch, 2012 .

Audio-only Bird Classification Using Unsupervised Feature Learning

Dan Stowell, Mark D. Plumbley, 2014, CLEF.

Framewise heterodyne chirp analysis of birdsong

Dan Stowell, Mark D. Plumbley, 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

Cross-task learning for audio tagging, sound event detection and spatial localization: DCASE 2019 baseline systems pdf

Yong Xu, Mark D. Plumbley, Qiuqiang Kong, 2019, ArXiv.

Recognition of harmonic sounds in polyphonic audio using a missing feature approach

Mark D. Plumbley, Anssi Klapuri, Dimitrios Giannoulis, 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

Polyphonic music transcription by non-negative sparse coding of power spectra

Mark D. Plumbley, Samer M. Abdallah, S. Abdallah, 2004 .

AUTOMATED RHYTHMIC TRANSFORMATION OF MUSICAL AUDIO

Matthew E. P. Davies, Mark D. Plumbley, Juan Pablo Bello, 2008 .

Hierarchical Learning for DNN-Based Acoustic Scene Classification pdf

Qiang Huang, Yong Xu, Mark D. Plumbley, 2016, DCASE.

Multivariate iterative hard thresholding for sparse decomposition with flexible sparsity patterns

Mark D. Plumbley, Wenwu Wang, Lucas Rencker, 2017, 2017 25th European Signal Processing Conference (EUSIPCO).

Binaural and log-power spectra features with deep neural networks for speech-noise separation

Yong Xu, Mark D. Plumbley, Wenwu Wang, 2017, 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP).

Real Time Object Based Coding

Mark D. Plumbley, Mark Sandler, Paul Brossier, 2003 .

Convolutive Blind Source Separation of Speech Signals in the Low Frequency Bands

Mark D. Plumbley, Maria G. Jafari, 2007 .

MUSIC REMIXING AND UPMIXING USING SOURCE SEPARATION

Mark D. Plumbley, Andrew J. R. Simpson, Emad M. Grais, 2016 .

Discourse Analysis Evaluation Method for Expressive Musical Interfaces

Mark D. Plumbley, Nick Bryan-Kinns, Dan Stowell, 2008, NIME.

Fast Multidimensional Entropy Estimation by $k$-d Partitioning

Dan Stowell, Mark D. Plumbley, D. Stowell, 2009, IEEE Signal Processing Letters.

IEEE AASP CASA Challenge - Public Dataset for Scene Classification Task

Dan Stowell, Mark D. Plumbley, Emmanouil Benetos, 2012 .

Discriminative Enhancement for Single Channel Audio Source Separation Using Deep Neural Networks

Mark D. Plumbley, Andrew J. R. Simpson, Emad M. Grais, 2016, LVA/ICA.

Learning Incoherent Subspaces: Classification via Incoherent Dictionary Learning

Mark D. Plumbley, Daniele Barchiesi, D. Barchiesi, 2014, J. Signal Process. Syst..

Online connectionist Q-learning produces unreliable performance with a synonym finding task

Mark D. Plumbley, Ian Johnson, Ian Johnson, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

A Contextual Study of Semantic Speech Editing in Radio Production

Mark D. Plumbley, Janko Calic, David M. Frohlich, 2018, Int. J. Hum. Comput. Stud..

Assessment of musical noise using localization of isolated peaks in time-frequency domain

Mark D. Plumbley, Wenwu Wang, Valentin Emiya, 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Behavior of greedy sparse representation algorithms on nested supports

Bob L. Sturm, Mark D. Plumbley, Boris Mailhé, 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

Measuring the Performance of Beat Tracking Algorithms Using a Beat Error Histogram

Matthew E. P. Davies, Mark D. Plumbley, Norberto Degara, 2011, IEEE Signal Processing Letters.

Audio Set Classification with Attention Model: A Probabilistic Perspective

Yong Xu, Mark D. Plumbley, Qiuqiang Kong, 2017, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Transcription of vocal melodies using voice characteristics and algorithm fusion

Emmanuel Vincent, Mark D. Plumbley, Juan Pablo Bello, 2006 .

Latent Variable Analysis and Signal Separation: 14th International Conference, LVA/ICA 2018, Guildford, UK, July 2–5, 2018, Proceedings

Mark D. Plumbley, Sharon Gannot, Yannick Deville, 2018 .

Attention-based Atrous Convolutional Neural Networks: Visualisation and Understanding Perspectives of Acoustic Scenes

Mark D. Plumbley, Zhao Ren, Qiuqiang Kong, 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Estimating Phase Linearity in the Frequency-Domain ICA Demixing Matrix

Mark D. Plumbley, Keisuke Toyama, 2009, ICA.

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

Yong Xu, Mark D. Plumbley, Qiuqiang Kong, 2017, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Perception of phase changes in the context of musical audio source separation

Mark D. Plumbley, Russell Mason, Emad M. Grais, 2018 .

Signal Processing, Psychoacoustic Engineering and Digital Worlds: Interdisciplinary Audio Research at the University of Surrey

Mark D. Plumbley, David Frohlich, Tim Brookes, 2017 .

Sparse representations of polyphonic music

Mark D. Plumbley, Mike E. Davies, Thomas Blumensath, 2006, Signal Process..

An alternating descent algorithm for the off-grid DOA estimation problem with sparsity constraints

Aris Gretsistas, Mark D. Plumbley, 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

Inexact Proximal Operators for $\ell_{p}$-Quasinorm Minimization

Mark D. Plumbley, Cian O'Brien, 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Learning the Mapping Function from Voltage Amplitudes to Sensor Positions in 3D-EMA Using Deep Neural Networks

Mark D. Plumbley, Christian Kroos, Christian Kroos, 2017, INTERSPEECH.

Singing voice separation using deep neural networks and f0 estimation

Mark D. Plumbley, Andrew J. R. Simpson, Emad M. Grais, 2016 .

Improving Reverberant Speech Separation with Binaural Cues Using Temporal Context and Convolutional Neural Networks

Yong Xu, Mark D. Plumbley, Qiuqiang Kong, 2018, LVA/ICA.

Chime-home: A dataset for sound source recognition in a domestic environment

Jon Barker, Sacha Krstulovic, Mark D. Plumbley, 2015, 2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA).

Fast Dictionary Learning for Sparse Representations of Speech Signals

Mark D. Plumbley, Maria G. Jafari, M. Jafari, 2011, IEEE Journal of Selected Topics in Signal Processing.

Polyphonic Sound Event Tracking Using Linear Dynamical Systems

Mathieu Lagrange, Mark D. Plumbley, Emmanouil Benetos, 2017, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

Learning Incoherent Dictionaries for Sparse Approximation Using Iterative Projections and Rotations

Mark D. Plumbley, Daniele Barchiesi, D. Barchiesi, 2013, IEEE Transactions on Signal Processing.

Predicting the perceived level of reverberation using machine learning

Mark D. Plumbley, Saeid Safavi, Wenwu Wang, 2018, 2018 52nd Asilomar Conference on Signals, Systems, and Computers.

Mark D. Plumbley, Mark Sandler, Juan Pablo Bello, 2001 .

SMALLbox - An Evaluation Framework for Sparse Representations and Dictionary Learning Algorithms

Matthew E. P. Davies, Ivan Damnjanovic, Mark D. Plumbley, 2010, LVA/ICA.

Separation of stereo speech signals based on a sparse dictionary algorithm

Mark D. Plumbley, Maria G. Jafari, 2008, 2008 16th European Signal Processing Conference.

The Role of High Frequencies in Convolutive Blind Source Separation of Speech Signals

Mark D. Plumbley, Maria G. Jafari, 2007, ICA.

A Multichannel Spatial Compressed Sensing Approach for Direction of Arrival Estimation

Aris Gretsistas, Mark D. Plumbley, 2010, LVA/ICA.

Causal Prediction of Continuous-Valued Music Features

Mark D. Plumbley, Anssi Klapuri, Peter Foster, 2011, ISMIR.

Geometry and Manifolds for Independent Component Analysis

Mark D. Plumbley, 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

Causal Tempo Tracking of Audio

Matthew E. P. Davies, Mark D. Plumbley, M. Davies, 2004, ISMIR.

Software Techniques for Good Practice in Audio and Music Research

Mark D. Plumbley, Chris Cannam, Luis A. Figueira, 2013 .

Probabilistic time-frequency source-filter decomposition of non-stationary signals

Mark D. Plumbley, Roland Badeau, 2013, 21st European Signal Processing Conference (EUSIPCO 2013).

Designing Neural Networks using a Genetic Rule-based System

Mark D. Plumbley, Kwok Ching Tsui, 1998 .

Structured sparsity using backwards elimination for Automatic Music Transcription

Mark D. Plumbley, Ken O'Hanlon, Nicolas Keriven, 2013, 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

BEAT TRACKING WITH A TWO STATE MODEL

Matthew E. P. Davies, Mark D. Plumbley, 2005 .

Sparse Coding for Convolutive Blind Audio Source Separation

Mark D. Plumbley, Mike E. Davies, Samer A. Abdallah, 2006, ICA.

Oracle estimators for the benchmarking of source separation algorithms

Rémi Gribonval, Emmanuel Vincent, Mark D. Plumbley, 2007, Signal Process..

A measure of statistical complexity based on predictive information pdf

Mark D. Plumbley, Samer A. Abdallah, S. Abdallah, 2010, ArXiv.

Weakly labelled AudioSet Classification with Attention Neural Networks.

Mark D. Plumbley, Qiuqiang Kong, Turab Iqbal, 2019 .

IEEE AASP CASA Challenge - Development Dataset for Event Detection Task (subtask OL)

Dan Stowell, Mark D. Plumbley, Emmanouil Benetos, 2012 .

Accounting for phase cancellations in non-negative matrix factorization using weighted distances

Mark D. Plumbley, Mark B. Sandler, Sebastian Ewert, 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Synchronizing Sequencing Software to a Live Drummer

Mark D. Plumbley, Andrew Robertson, 2013, Computer Music Journal.

Cybernetics and Systems

Mark D. Plumbley, Mike E. Davies, Mark Sandler, 2018 .

Large‐scale analysis of frequency modulation in birdsong data bases pdf

Mark D. Plumbley, Dan Stowell, D. Stowell, 2013, ArXiv.

Recovery of Sparse Representations by Polytope Faces Pursuit

Mark D. Plumbley, 2006, ICA.

Psychophysical Evaluation of Audio Source Separation Methods

Mark D. Plumbley, Andrew J. R. Simpson, Emad M. Grais, 2017, LVA/ICA.

Remixing musical audio on the web using source separation

Mark D. Plumbley, Andrew J. R. Simpson, Emad M. Grais, 2016 .

Perceptual Evaluation of Source Separation for Remixing Music

Mark D. Plumbley, Hagen Wierstorf, Christopher Hummersone, 2017 .

Audio source separation with a signal-adaptive local cosine transform

Mark D. Plumbley, Mike E. Davies, Andrew Nesbit, 2007, Signal Process..

The Medium is the Message: Composing Instruments and Performing Mappings

Mark D. Plumbley, Tim Murray Browne, Nick Bryan-Kinns, 2011, NIME.

The effect of receptor signal-to-noise levels on optimal filtering in a sensory system

Mark D. Plumbley, Frank Fallside, 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

Audio source separation with deep neural networks using the dropout algorithm

Yong Xu, Mark D. Plumbley, Qiuqiang Kong, 2017 .

Hierarchical Learning for DNN-Based Acoustic Scene Classification

Mark D. Plumbley, Qiang Huang, Yong Xu, 2016 .

Low Bit-Rate Object Coding of Musical Audio Using Bayesian Harmonic Models

Emmanuel Vincent, Mark D. Plumbley, E. Vincent, 2007, IEEE Transactions on Audio, Speech, and Language Processing.

Speech denoising based on a greedy adaptive dictionary algorithm

Mark D. Plumbley, Maria G. Jafari, M. Jafari, 2009, 2009 17th European Signal Processing Conference.

Real Time Gesture Learning and Recognition: Towards Automatic Categorization

Mark D. Plumbley, Samer A. Abdallah, Nick Bryan-Kinns, 2008, NIME.

INVESTIGATING SINGLE-CHANNEL AUDIO SOURCE SEPARATION METHODS BASED ON NON-NEGATIVE MATRIX FACTORIZATION

Mark D. Plumbley, Beiming Wang, Beiming Wang, 2006 .

Generation and adaptation of neural networks by evolutionary techniques (GANNET)

Mark D. Plumbley, Richard W. Prager, Frank Fallside, 2005, Neural Computing & Applications.

Non-Negative Group Sparsity with Subspace Note Modelling for Polyphonic Transcription

Mark D. Plumbley, Hidehisa Nagano, Ken O'Hanlon, 2016, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

Approaches to Complex Sound Scene Analysis

Dan Stowell, Mark D. Plumbley, Emmanouil Benetos, 2018 .

Oracle estimation of adaptive cosine packet transforms for underdetermined audio source separation

Mark D. Plumbley, Andrew Nesbit, 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

Extension of Sparse, Adaptive Signal Decompositions to Semi-blind Audio Source Separation

Emmanuel Vincent, Mark D. Plumbley, Andrew Nesbit, 2009, ICA.

Context-Dependent Beat Tracking of Musical Audio

Matthew E. P. Davies, Mark D. Plumbley, 2007, IEEE Transactions on Audio, Speech, and Language Processing.

Using phase linearity in frequency-domain ICA to tackle the permutation problem

Mark D. Plumbley, Keisuke Toyama, 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

An Open Dataset for Research on Audio Field Recording Archives: freefield1010 pdf

Dan Stowell, Mark D. Plumbley, D. Stowell, 2013, Semantic Audio.

INK-SVD: Learning incoherent dictionaries for sparse representations

Mark D. Plumbley, Boris Mailhé, Daniele Barchiesi, 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Robustness and independence of voice timbre features under live performance acoustic degradations

Dan Stowell, Mark D. Plumbley, 2008 .

Towards musical instrument separation using multiple-cause neural networks

Mark D. Plumbley, P Pajunen, P. Pajunen, 2000 .

Exploring the effect of rhythmic style classification on automatic tempo estimation

Matthew E. P. Davies, Mark D. Plumbley, 2008, 2008 16th European Signal Processing Conference.

Orthogonality-Regularized Masked NMF for Learning on Weakly Labeled Audio Data

Mark D. Plumbley, Lucas Rencker, Iwona Sobieraj, 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Analysis-based sparse reconstruction with synthesis-based solvers

Mark D. Plumbley, Nicolae Cleju, Maria G. Jafari, 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Onset Event Decoding Exploiting the Rhythmic Structure of Polyphonic Music

Matthew E. P. Davies, Mark D. Plumbley, Norberto Degara, 2011, IEEE Journal of Selected Topics in Signal Processing.

ADAPTIVE WHITENING PREPROCESSING APPLIED TO ONSET DETECTORS, MIREX 2007

Dan Stowell, Mark D. Plumbley, 2007 .

Benchmarking flexible adaptive time-frequency transforms for underdetermined audio source separation

Emmanuel Vincent, Mark D. Plumbley, Andrew Nesbit, 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

Joint detection and classification convolutional neural network on weakly labelled bird audio detection

Yong Xu, Mark D. Plumbley, Qiuqiang Kong, 2017, 2017 25th European Signal Processing Conference (EUSIPCO).

Theorems on Positive Data: On the Uniqueness of NMF

Mark D. Plumbley, Lars Kai Hansen, Søren Holdt Jensen, 2008, Comput. Intell. Neurosci..

Predominant-F0 estimation using Bayesian harmonic waveform models

Emmanuel Vincent, Mark D. Plumbley, 2005 .

A "nonnegative PCA" algorithm for independent component analysis

Erkki Oja, Mark D. Plumbley, E. Oja, 2004, IEEE Transactions on Neural Networks.

Using Oracle Analysis for Decomposition-Based Automatic Music Transcription

Mark D. Plumbley, Hidehisa Nagano, Ken O'Hanlon, 2012, CMMR.

Phase-based harmonic/percussive separation

Mark D. Plumbley, Christian Dittmar, Estefanía Cano, 2014, INTERSPEECH.

Detection of overlapping acoustic events using a temporally-constrained probabilistic model

Mathieu Lagrange, Mark D. Plumbley, Emmanouil Benetos, 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Real-time Visual Beat Tracking using a Comb Filter Matrix

Mark D. Plumbley, Andrew Robertson, Adam M. Stark, 2011, ICMC.

Improved multiple birdsong tracking with distribution derivative method and Markov renewal process clustering

Dan Stowell, Mark D. Plumbley, Jordi Bonada, 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

Preliminary guidelines for subjective evalutation of audio source separation algorithms

Emmanuel Vincent, Mark D. Plumbley, Maria G. Jafari, 2006 .

Audio Commons: bringing Creative Commons audio content to the creative industries

Mark D. Plumbley, Xavier Serra, Tim Brookes, 2016 .

Lyapunov functions for convergence of principal component algorithms

Mark D. Plumbley, 1995, Neural Networks.

Keynote addresses: Reverse engineering the neural mechanisms involved in robust speech processing

Mark D. Plumbley, Nima Mesgarani, 2015, 2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA).

Automatic Music Transcription and Audio Source Separation

Mark D. Plumbley, Mark B. Sandler, Mike E. Davies, 2002, Cybern. Syst..

Blind Separation of Positive Sources by Globally Convergent Gradient Search

Erkki Oja, Mark D. Plumbley, E. Oja, 2004, Neural Computation.

Non-negative matrix factorisation incorporating greedy hellinger sparse coding applied to polyphonic music transcription

Mark D. Plumbley, Mark B. Sandler, Ken O'Hanlon, 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Supporting Audiography: Design of a System for Sentimental Sound Recording, Classification and Playback

Yong Xu, Mark D. Plumbley, Tijs Duel, 2018, HCI.

Audio Effects for Real-Time Performance Using Beat Tracking

Matthew E. P. Davies, Mark D. Plumbley, Adam M. Stark, 2007 .

Sequential minimal eigenvalues - an approach to analysis dictionary learning

Michael Elad, Mark D. Plumbley, Boaz Ophir, 2011, 2011 19th European Signal Processing Conference.

Multichannel High-Resolution NMF for Modeling Convolutive Mixtures of Non-Stationary Signals in the Time-Frequency Domain

Mark D. Plumbley, Roland Badeau, R. Badeau, 2014, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

A new hillclimber for classifier systems

Mark D. Plumbley, Kwok Ching Tsui, 1997 .

Riemannian Optimization Method on the Flag Manifold for Independent Subspace Analysis

Mark D. Plumbley, Shotaro Akaho, Yasunori Nishimori, 2006, ICA.

Consistent Dictionary Learning for Signal Declipping

Mark D. Plumbley, Wenwu Wang, Francis Bach, 2018, LVA/ICA.

Fast labelling of notes in music signals

Mark D. Plumbley, Juan Pablo Bello, Paul Brossier, 2004, ISMIR.

Improving instrument recognition in polyphonic music through system integration

Anssi Klapuri, Mark D. Plumbley, Emmanouil Benetos, 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Segregating event streams and noise with a Markov renewal process model

Dan Stowell, Mark D. Plumbley, D. Stowell, 2012, J. Mach. Learn. Res..

Communications and neural networks: theory and practice

Mark D. Plumbley, 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

Performance following: Tracking a performance without a score

Mark D. Plumbley, Adam M. Stark, 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

The Serendiptichord: A Wearable Instrument for Contemporary Dance Performance

Mark D. Plumbley, Tim Murray-Browne, Di Mainstone, 2010 .

B-Keeper: a beat-tracker for live performance

Mark D. Plumbley, Andrew Robertson, 2007, NIME '07.

A constrained matching pursuit approach to audio declipping

Michael Elad, Rémi Gribonval, Mark D. Plumbley, 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Information-Theoretic Approaches to Neural Network Learning

Mark D. Plumbley, 2019, Neural Network Perspectives on Cognition and Adaptive Robotics.

Acoustic Scene Classification: Classifying environments from the sounds they produce

Mark D. Plumbley, Daniele Barchiesi, Dan Stowell, 2014, IEEE Signal Processing Magazine.

Cross-associating unlabelled timbre distributions to create expressive musical mappings

Dan Stowell, Mark D. Plumbley, 2010, WAPA.

Attention and Localization Based on a Deep Convolutional Recurrent Model for Weakly Supervised Audio Tagging

Qiang Huang, Yong Xu, Mark D. Plumbley, 2017, INTERSPEECH.

Introduction to sound scene and event analysis

Mark D. Plumbley, Daniel P. W. Ellis, Tuomas Virtanen, 2018 .

Information Theory and Neural Networks

Mark D. Plumbley, John G. Taylor, John G. Taylor, 1993 .

Low-rank matrix completion based malicious user detection in cooperative spectrum sensing

Yue Gao, Zhijin Qin, Laurie G. Cuthbert, 2013, 2013 IEEE Global Conference on Signal and Information Processing.

On Theorem 10 in "On Polar Polytopes and the Recovery of Sparse Representations" [Sep 07 3188-3195]

Bob L. Sturm, Mark D. Plumbley, Boris Mailhé, 2013, IEEE Trans. Inf. Theory.

Efficient Bayesian inference for harmonic models via adaptive posterior factorization

Emmanuel Vincent, Mark D. Plumbley, 2008, Neurocomputing.

Reliability-Informed Beat Tracking of Musical Signals

Matthew E. P. Davies, Mark D. Plumbley, Enrique Argones-Rúa, 2012, IEEE Transactions on Audio, Speech, and Language Processing.

Open-Window: A Sound Event Data Set For Window State Detection And Recognition

Mark D. Plumbley, Turab Iqbal, Philip Coleman, 2020 .

Using deep neural networks to estimate tongue movements from speech face motion

Mark D. Plumbley, Christian Kroos, Catherine T. Best, 2017, AVSP.

Deep Remix: Remixing Musical Mixtures Using a Convolutional Deep Neural Network pdf

Mark D. Plumbley, Andrew J. R. Simpson, Gerard Roma, 2015, ArXiv.

ICA Research Network International Workshop

Mark D. Plumbley, Mike E. Davies, Andrew Nesbit, 2006 .

A joint detection-classification model for audio tagging of weakly labelled data

Yong Xu, Mark D. Plumbley, Qiuqiang Kong, 2016, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Single channel audio source separation using convolutional denoising autoencoders

Mark D. Plumbley, Emad M. Grais, 2017, 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

A Demonstration of Hierarchical Structure Usage in Expressive Timing Analysis by Model Selection Tests

Mark D. Plumbley, Simon Dixon, Shengchen Li, 2018, 2018 37th Chinese Control Conference (CCC).

A greedy algorithm with learned statistics for sparse signal reconstruction

Mark D. Plumbley, Wenwu Wang, Lucas Rencker, 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Automatic Music Transcription using row weighted decompositions

Mark D. Plumbley, Ken O'Hanlon, 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

Choosing analysis or synthesis recovery for sparse reconstruction

Mark D. Plumbley, Nicolae Cleju, Maria G. Jafari, 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

Dictionary learning of convolved signals

Mark D. Plumbley, Daniele Barchiesi, 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

A database and challenge for acoustic scene classification and event detection

Dan Stowell, Mathieu Lagrange, Mathias Rossignol, 2013, 21st European Signal Processing Conference (EUSIPCO 2013).

Separating sources from sequentially acquired mixtures of heart signals

Mark D. Plumbley, Miguel Tavares Coimbra, Sandra da Silva Mattos, 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Weakly Labelled AudioSet Tagging With Attention Neural Networks

Mark D. Plumbley, Qiuqiang Kong, Turab Iqbal, 2019, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

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

Mathieu Lagrange, Mark D. Plumbley, Emmanouil Benetos, 2018, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

Optimization Using Fourier Expansion over a Geodesic for Non-negative ICA

Mark D. Plumbley, 2004, ICA.

Neuroevolution for sound event detection in real life audio: A pilot study

Mark D. Plumbley, Christian Kroos, C. Kroos, 2017 .

A Hebbian/anti-Hebbian network which optimizes information capacity by orthonormalizing the principal subspace

Mark D. Plumbley, 1993 .

An L1 criterion for dictionary learning by subspace identification

Rémi Gribonval, Mark D. Plumbley, Hadi Zayyani, 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

Deep Karaoke: Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network

Mark D. Plumbley, Andrew J. R. Simpson, Gerard Roma, 2015, LVA/ICA.

On Polar Polytopes and the Recovery of Sparse Representations pdf

Mark D. Plumbley, 2007, IEEE Trans. Inf. Theory.

Estimating the loudness balance of musical mixtures using audio source separation

Mark D. Plumbley, Hagen Wierstorf, Christopher Hummersone, 2017 .

Analysis SimCO Algorithms for Sparse Analysis Model Based Dictionary Learning

Wei Dai, Mark D. Plumbley, Jonathon A. Chambers, 2016, IEEE Transactions on Signal Processing.

Audio Inpainting

Michael Elad, Rémi Gribonval, Mark D. Plumbley, 2012, IEEE Transactions on Audio, Speech, and Language Processing.

Multi-Band Multi-Resolution Fully Convolutional Neural Networks for Singing Voice Separation

Fei Zhao, Mark D. Plumbley, Emad M. Grais, 2019, 2020 28th European Signal Processing Conference (EUSIPCO).

Real-time temporal segmentation of note objects in music signals

Mark D. Plumbley, Juan Pablo Bello, Paul Brossier, 2004, ICMC.

Beat Tracking Towards Automatic Musical Accompaniment

Matthew E. P. Davies, Mark D. Plumbley, Paul Brossier, 2005 .

Sound Event Detection with Sequentially Labelled Data Based on Connectionist Temporal Classification and Unsupervised Clustering

Mark D. Plumbley, Qiuqiang Kong, Shengchen Li, 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Dictionary learning via projected maximal exploration

Mark D. Plumbley, Boris Mailhé, 2013, 2013 IEEE Global Conference on Signal and Information Processing.

Towards a musical beat emphasis function

Matthew E. P. Davies, Douglas Eck, Mark D. Plumbley, 2009, 2009 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.

Dictionary Learning for L1-Exact Sparse Coding

Mark D. Plumbley, 2007, ICA.

Blind Source Separation using Space-Time Independent Component Analysis

Mark D. Plumbley, Mike E. Davies, Samer A. Abdallah, 2007, Blind Speech Separation.

Rendering Audio Using Expressive MIDI

Mark D. Plumbley, Stephen J. Welburn, 2009 .

Rhythmic Analysis for Real-Time audio effects

Matthew E. P. Davies, Mark D. Plumbley, Adam M. Stark, 2008, ICMC.

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

Zhijin Qin, Mark D. Plumbley, Clive Parini, 2016, IEEE Transactions on Signal Processing.

Oracle evaluation of flexible adaptive transforms for underdetermined audio source separation

Emmanuel Vincent, Mark D. Plumbley, Andrew Nesbit, 2008 .

Sound Event Detection and Time–Frequency Segmentation from Weakly Labelled Data

Yong Xu, Mark D. Plumbley, Qiuqiang Kong, 2018, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

Dictionary Learning with Large Step Gradient Descent for Sparse Representations

Mark D. Plumbley, Boris Mailhé, 2012, LVA/ICA.

Analysis of an Unsupervised Indirect Feedback Network

Mark D. Plumbley, 1994 .

Referenceless Performance Evaluation of Audio Source Separation using Deep Neural Networks

Mark D. Plumbley, Hagen Wierstorf, Russell Mason, 2018, 2019 27th European Signal Processing Conference (EUSIPCO).

Combining Mask Estimates for Single Channel Audio Source Separation Using Deep Neural Networks

Mark D. Plumbley, Andrew J. R. Simpson, Emad M. Grais, 2016, INTERSPEECH.

A Turing Test for B-Keeper: Evaluating an Interactive

Mark D. Plumbley, Nick Bryan-Kinns, Andrew Robertson, 2008, NIME.

A Joint Separation-Classification Model for Sound Event Detection of Weakly Labelled Data

Yong Xu, Mark D. Plumbley, Qiuqiang Kong, 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Improving the Performance of Pitch Estimators

Mark D. Plumbley, Stephen J. Welburn, 2010 .

Combining Fully Convolutional and Recurrent Neural Networks for Single Channel Audio Source Separation

Mark D. Plumbley, Emad M. Grais, 2018 .

Malicious User Detection Based on Low-Rank Matrix Completion in Wideband Spectrum Sensing

Zhijin Qin, Mark D. Plumbley, Yue Gao, 2018, IEEE Transactions on Signal Processing.

Source extraction from two-channel mixtures by joint cosine packet analysis

Mark D. Plumbley, Mark B. Sandler, Mike E. Davies, 2006, 2006 14th European Signal Processing Conference.

Predictive Information in Gaussian Processes with Application to Music Analysis

Mark D. Plumbley, Samer A. Abdallah, S. Abdallah, 2013, GSI.

Note onset detection using rhythmic structure

Matthew E. P. Davies, Mark D. Plumbley, Norberto Degara, 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

BSS Oracle Toolbox Version 2.1 User Guide

Emmanuel Vincent, Mark D. Plumbley, E. Vincent, 2007 .

Polyphonic piano transcription using non-negative Matrix Factorisation with group sparsity

Mark D. Plumbley, Ken O'Hanlon, 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Use of Audio Editors in Radio Production

Mark D. Plumbley, Janko Calic, Chris Baume, 2015 .

Cognitive music modelling: An information dynamics approach

Mark D. Plumbley, Henrik Ekeus, Samer A. Abdallah, 2012, 2012 3rd International Workshop on Cognitive Information Processing (CIP).

Direct Approaches to Improving the Robustness of Multilayer Neural Networks

Guido Bugmann, Petr Sojka, Mark D. Plumbley, 1992 .

The Clustering of Expressive Timing Within a Phrase in Classical Piano Performances by Gaussian Mixture Models

Mark D. Plumbley, Dawn A. A. Black, Shengchen Li, 2015, CMMR.

A doubly sparse greedy adaptive dictionary learning algorithm for music and large-scale data

Mark D. Plumbley, Maria G. Jafari, 2010 .

Latent Variable Analysis and Signal Separation

Mark D. Plumbley, Sharon Gannot, Russell Mason, 2018, Lecture Notes in Computer Science.

Real-Time Chord Recognition for Live Performance

Mark D. Plumbley, Adam M. Stark, 2009, ICMC.

Deep Neural Network Baseline for DCASE Challenge 2016

Mark D. Plumbley, Qiuqiang Kong, Wenwu Wang, 2016, DCASE.

Group Polytope Faces Pursuit for Recovery of Block-Sparse Signals

Aris Gretsistas, Mark D. Plumbley, 2012, LVA/ICA.

Acoustic Event Detection from Weakly Labeled Data Using Auditory Salience

Mark D. Plumbley, William J. Davies, Bruno Fazenda, 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Adaptive whitening for Improved Real-Time audio onset Detection

Dan Stowell, Mark D. Plumbley, D. Stowell, 2007, ICMC.

Musical audio analysis using sparse representations

Emmanuel Vincent, Mark D. Plumbley, Thomas Blumensath, 2006 .

Surrey-cvssp system for DCASE2017 challenge task4 pdf

Yong Xu, Mark D. Plumbley, Qiuqiang Kong, 2017, ArXiv.

Big Data for Musicology

Tillman Weyde, Mark D. Plumbley, Dan Tidhar, 2014, DLfM '14.

An adaptive stereo basis method for convolutive blind audio source separation

Emmanuel Vincent, Mark D. Plumbley, Mike E. Davies, 2008, Neurocomputing.

Lie Group Methods for Optimization with Orthogonality Constraints

Mark D. Plumbley, 2004, ICA.

REAL-TIME BEAT-TRACKER FOR LIVE PERFORMANCE WITH DRUMS

Mark D. Plumbley, Andrew Robertson, Andrew Robertson, 2007 .

MIREX 2009 AUDIO BEAT TRACKING EVALUATION: DAVIES, ROBERTSON AND PLUMBLEY

Matthew E. P. Davies, Mark D. Plumbley, Andrew Robertson, 2009 .

Algorithms for nonnegative independent component analysis

Mark D. Plumbley, 2003, IEEE Trans. Neural Networks.

IF THE INDEPENDENT COMPONENTS OF NATURAL IMAGES ARE EDGES, WHAT ARE THE INDEPENDENT COMPONENTS OF NATURAL SOUNDS?

Mark D. Plumbley, Samer A. Abdallah, S. Abdallah, 2001 .

Multichannel HR-NMF for modelling convolutive mixtures of non-stationary signals in the time-frequency domain

Mark D. Plumbley, Roland Badeau, 2013, 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.

Instrumentation-based music similarity using sparse representations

Mark D. Plumbley, Hiromasa Fujihara, Anssi Klapuri, 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

BSS Eval or Peass? Predicting the Perception of Singing-Voice Separation

Mark D. Plumbley, Hagen Wierstorf, Emad M. Grais, 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Score informed audio source separation using constrained nonnegative matrix factorization and score synthesis

Mark D. Plumbley, Joachim Fritsch, 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

Geometrical methods for non-negative ICA: Manifolds, Lie groups and toral subalgebras

Mark D. Plumbley, 2005, Neurocomputing.

Flag Manifolds for Subspace ICA Problems

Mark D. Plumbley, Shotaro Akaho, Samer A. Abdallah, 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

Synthesis of Images by Two-Stage Generative Adversarial Networks

Qiang Huang, Mark D. Plumbley, Philip J. B. Jackson, 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Performance Following: Real-Time Prediction of Musical Sequences Without a Score

Mark D. Plumbley, Adam M. Stark, 2012, IEEE Transactions on Audio, Speech, and Language Processing.

Sound Software: Towards software reuse in audio and music research

Mark D. Plumbley, Luís Figueira, Chris Cannam, 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio Source Separation pdf

Mark D. Plumbley, Hagen Wierstorf, Emad M. Grais, 2018, LVA/ICA.

Sound Source Separation

Emmanuel Vincent, Mark D. Plumbley, Gianpaolo Evangelista, 2011 .

Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging

Qiang Huang, Yong Xu, Mark D. Plumbley, 2016, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

Event-based Multitrack Alignment using a Probabilistic Framework

Mark D. Plumbley, Andrew Robertson, Andrew Robertson, 2015 .

Automatic music transcription using low rank non-negative matrix decomposition

Mark D. Plumbley, Cian O'Brien, Cian O'Brien, 2017, 2017 25th European Signal Processing Conference (EUSIPCO).

Big Chord Data Extraction and Mining

Daniel Wolff, Tillman Weyde, Mark D. Plumbley, 2014 .

Clustering Expressive Timing with Regressed Polynomial Coefficients Demonstrated by a Model Selection Test

Mark D. Plumbley, Simon Dixon, Shengchen Li, 2017, ISMIR.

Acoustic Scene Generation with Conditional Samplernn

Yong Xu, Mark D. Plumbley, Qiuqiang Kong, 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Musical Source Separation: An Introduction

Mark D. Plumbley, Antoine Liutkus, Derry FitzGerald, 2019, IEEE Signal Processing Magazine.

Natural Conjugate Gradient on Complex Flag Manifolds for Complex Independent Subspace Analysis

Mark D. Plumbley, Shotaro Akaho, Yasunori Nishimori, 2008, ICANN.

Learning overcomplete dictionaries with ℓ0-sparse Non-negative Matrix Factorisation

Mark D. Plumbley, Ken O'Hanlon, K. O'Hanlon, 2013, 2013 IEEE Global Conference on Signal and Information Processing.

A dynamic programming variant of non-negative matrix deconvolution for the transcription of struck string instruments

Mark D. Plumbley, Mark B. Sandler, Sebastian Ewert, 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Real-time beat-synchronous audio effects

Matthew E. P. Davies, Mark D. Plumbley, Adam M. Stark, 2007, NIME '07.

Information Theory And Sensory Perception

Mark D. Plumbley, Samer Abdallah, S. Abdallah, 2006 .

Learning incoherent subspaces for classification via supervised iterative projections and rotations

Mark D. Plumbley, Daniele Barchiesi, D. Barchiesi, 2013, 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

Polyphonic transcription by non-negative sparse coding of power spectra

Mark D. Plumbley, Samer M. Abdallah, S. Abdallah, 2004, ISMIR.

IEEE AASP CASA Challenge - Linux System Image

Dan Stowell, Mark D. Plumbley, Emmanouil Benetos, 2013 .

DCASE 2018 Challenge Surrey cross-task convolutional neural network baseline

Yong Xu, Mark D. Plumbley, Qiuqiang Kong, 2018, DCASE.

Raw Multi-Channel Audio Source Separation using Multi- Resolution Convolutional Auto-Encoders

Mark D. Plumbley, Emad M. Grais, Dominic Ward, 2018, 2018 26th European Signal Processing Conference (EUSIPCO).

Efficient information transfer and anti-Hebbian neural networks

Mark D. Plumbley, 1993, Neural Networks.

Masked non-negative matrix factorization for eire detection using weakly labeled data

Mark D. Plumbley, Qiuqiang Kong, Iwona Sobieraj, 2017, 2017 25th European Signal Processing Conference (EUSIPCO).

A Reproducible Research Framework for Audio Inpainting

Michael Elad, Mark D. Plumbley, Valentin Emiya, 2011 .

Identification of dental bacteria using statistical and neural approaches

Mark D. Plumbley, Choo Min Lim, R. Davidson, 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

Fast tagging of natural sounds using marginal co-regularization

Qiang Huang, Yong Xu, Mark D. Plumbley, 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Automatic Environmental Sound Recognition: Performance Versus Computational Cost

Sacha Krstulovic, Mark D. Plumbley, Siddharth Sigtia, 2016, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

Deep neural network based audio source separation

Yong Xu, Mark D. Plumbley, Wenwu Wang, 2016 .

IEEE AASP CASA Challenge - Training Dataset for Event Detection Task (subtasks OL, OS)

Dan Stowell, Mark D. Plumbley, Emmanouil Benetos, 2012 .

Post-Processing Fiddle~: A Real-Time Multi-Pitch Tracking Technique Using Harmonic Partial Subtraction for Use within Live Performance Systems

Mark D. Plumbley, Andrew Robertson, 2009, ICMC.

Acoustic detection of multiple birds in environmental audio by Matching Pursuit

Dan Stowell, Mark D. Plumbley, 2013 .

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

Dan Stowell, Mark D. Plumbley, D. Stowell, 2014, PeerJ.

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, Laurent Daudet, Rémi Gribonval, 2010 .

Information dynamics: patterns of expectation and surprise in the perception of music

Mark D. Plumbley, Samer M. Abdallah, S. Abdallah, 2009, Connect. Sci..

On the Use of Entropy for Beat Tracking Evaluation

Matthew E. P. Davies, Mark D. Plumbley, 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

Score-Informed Source Separation for Musical Audio Recordings: An overview

Mark D. Plumbley, Meinard Müller, Bryan Pardo, 2014, IEEE Signal Processing Magazine.

Single Channel Audio Source Separation using Deep Neural Network Ensembles

Mark D. Plumbley, Andrew J. R. Simpson, Emad M. Grais, 2016 .

Structured sparsity for automatic music transcription

Mark D. Plumbley, Hidehisa Nagano, Ken O'Hanlon, 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Single-Channel Mixture Decomposition Using Bayesian Harmonic Models

Emmanuel Vincent, Mark D. Plumbley, 2006, ICA.

On the disjointess of sources in music using different time-frequency representations

Mark D. Plumbley, Anssi Klapuri, Daniele Barchiesi, 2011, 2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA).

Blind source separation of periodic sources from sequentially recorded instantaneous mixtures

Mark D. Plumbley, Maria G. Jafari, Fabio L. Hedayioglu, 2011, 2011 7th International Symposium on Image and Signal Processing and Analysis (ISPA).

Matching live sources with physical models

Mark D. Plumbley, Mark Sandler, Paul Brossier, 2003 .

Convolutional gated recurrent neural network incorporating spatial features for audio tagging

Qiang Huang, Yong Xu, Mark D. Plumbley, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

Unsupervised analysis of polyphonic music by sparse coding

Mark D. Plumbley, Samer A. Abdallah, S. Abdallah, 2006, IEEE Transactions on Neural Networks.

Proceedings of International ICSC Symposium on Soft Computing and Intelligent Systems for Industry

Mark D. Plumbley, Mike E. Davies, Mark Sandler, 2002 .

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

Rémi Gribonval, Mark D. Plumbley, Mike E. Davies, 2010, Proceedings of the IEEE.

Denoising and segmentation of the second heart sound using matching pursuit

Mark D. Plumbley, Miguel Tavares Coimbra, Sandra da Silva Mattos, 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Fully Deep Neural Networks Incorporating Unsupervised Feature Learning for Audio Tagging pdf

Qiang Huang, Yong Xu, Mark D. Plumbley, 2016, ArXiv.

Polyphonic Sound Event Detection and Localization using a Two-Stage Strategy pdf

Mark D. Plumbley, Qiuqiang Kong, Wenwu Wang, 2019, DCASE.

Structure-aware non-negative dictionary learning

Mark D. Plumbley, Ken O'Hanlon, 2011 .

Sparse Analysis Model Based Dictionary Learning for Signal Declipping

Mark D. Plumbley, Wenwu Wang, Lucas Rencker, 2021, IEEE Journal of Selected Topics in Signal Processing.

Conditional Sound Generation Using Neural Discrete Time-Frequency Representation Learning

Mark D. Plumbley, Turab Iqbal, Wenwu Wang, 2021, 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP).

Using UMAP to Inspect Audio Data for Unsupervised Anomaly Detection under Domain-Shift Conditions pdf

Mark D. Plumbley, Andres Fernandez, 2021, ArXiv.

Sound Event Detection: A tutorial

Mark D. Plumbley, Tuomas Virtanen, Annamaria Mesaros, 2021, IEEE Signal Processing Magazine.

CL4AC: A Contrastive Loss for Audio Captioning pdf

Mark D. Plumbley, Wenwu Wang, Tom Ko, 2021, ArXiv.

Audio Captioning Transformer pdf

Mark D. Plumbley, Wenwu Wang, Qiushi Huang, 2021, DCASE.

Conditions for nonnegative independent component analysis

Mark D. Plumbley, 2002, IEEE Signal Processing Letters.

Algorithms for Non-Negative Independent Component Analysis

Mark D. Plumbley, 2002 .

BLIND SEPARATION OF POSITIVE SOURCES USING NON-NEGATIVE PC A

Mark D. Plumbley, E. Oja, E. London, 2003 .

Unsupervised onset detection : A probabilistic approach using ICA and a hidden Markov classifier

Mark D. Plumbley, S. Abdallah, 2003 .

PROBABILITY AS METADATA: EVENT DETECTION IN MUSIC USING ICA AS A CONDITIONAL DENSITY MODEL

Mark D. Plumbley, S. Abdallah, 2003 .

Clustering of Sparse Binary Data using a Minimum Description Length Approach

Mark D. Plumbley, 2002 .

predictive information , multi-information , and binding information

Mark D. Plumbley, S. Abdallah, 2010 .

Submitteed to the International Conference on Independent Component Analysis and Blind Signal Separation ( ICA 2001 ) ADAPTIVE LATERAL INHIBITION FOR NON-NEGATIVE ICA

Mark D. Plumbley, 2001 .

Probabilistic Modeling Paradigms for Audio Source Separation

Mark D. Plumbley, Mike E. Davies, Samer A. Abdallah, 2010 .

Geometry and homotopy for l 1 sparse representations

Mark D. Plumbley, 2005 .

Birdsong and C4DM: A survey of UK birdsong and machine recognition for music researchers

Mark D. Plumbley, D. Stowell, 2011 .

Towards Optimal Learning from Very Small Data Sets

Mark D. Plumbley, 1994 .

Audio inpainting: problem statement, relation with sparse representations and some experiments

Mark D. Plumbley, R. Gribonval, Michael Elad, 2010, LVA/ICA 2010.

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

Mark D. Plumbley, Yuxuan Wang, Qiuqiang Kong, 2019, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

COMPARING MID-LEVEL REPRESENTATIONS FOR AUDIO BASED BEAT TRACKING

Mark D. Plumbley, M. Davies, 2005 .

Chapter 1 Sound Source Separation

Mark D. Plumbley, E. Vincent, G. Evangelista, 2008 .

Sparse reconstruction for compressed sensing using Stagewise Polytope Faces Pursuit

Mark D. Plumbley, M. Bevilacqua, Marco Bevilacqua, 2009, 2009 16th International Conference on Digital Signal Processing.

Real-time beat-synchronous analysis of musical audio

Matthew E. P. Davies, Mark D. Plumbley, Adam M. Stark, 2009 .

Non-negative mixtures

Mark D. Plumbley, Andrzej Cichocki, Rasmus Bro, 2010 .

A Network Which Performs Orthonormalized Principal Subspace Extraction

Mark D. Plumbley, 1994 .

A Subspace Network That Determines Its Own Output Dimension

Mark D. Plumbley, 1994 .

Blind source separation of convolutive audio using an adaptive stereo basis

Emmanuel Vincent, Mark D. Plumbley, Mike E. Davies, 2006 .

Predictive Information Rate in Discrete-time Gaussian Processes

Mark D. Plumbley, Samer A. Abdallah, 2012 .

Feature design for multilabel bird song classification in noise ( NIPS 4 B challenge )

Dan Stowell, Mark D. Plumbley, 2013 .

Riemannian optimization method on generalized flag manifolds for complex and subspace ICA

Mark D. Plumbley, Shotaro Akaho, Yasunori Nishimori, 2006 .

Geodesic Learning Algorithms Over Flag Manifolds

Mark D. Plumbley, Y. Nishimori, S. Akaho, 2007 .

Coupled Sparse NMF vs. Random Forest Classification for Real Life Acoustic Event Detection

Mark D. Plumbley, Iwona Sobieraj, I. Sobieraj, 2016, DCASE.

ADAPTIVE LATERAL INHIBITION FOR NON-NEGATIVE ICA

Mark D. Plumbley, 2001 .

USING PHASE LINEARITY TO TACKLE THE PERMUTATION PROBLEM IN AUDIO SOURCE SEPARATION

Mark D. Plumbley, Maria G. Jafari, Andrew Nesbit, 2008 .

Characteristics of the beatboxing vocal style

Dan Stowell, Mark D. Plumbley, D. Stowell, 2008 .

Fixed points of dictionary learning algorithms for sparse representations

Mark D. Plumbley, Boris Mailhe, 2013 .

Experiments on musical instrument separation using multiple-cause models

Mark D. Plumbley, J Klingseisen, 2004 .

SOLVING THE PERMUTATION PROBLEM USING PHASE LINEARITY AND FREQUENCY CORRELATION

Mark D. Plumbley, Maria G. Jafari, Andrew Nesbit, 2008 .

Musical Instruments Identification System

Mark D. Plumbley, Li Xiang, 2006 .

Unsupervised Learning for Music

Mark D. Plumbley, PerceptionSamer Abdallah, Mark D. Plumbleyy, 1999 .

Information dynamics

Mark D. Plumbley, S. Abdallah, 1989 .

Predictive information and Bayesian surprise in exchangeable random processes

Mark D. Plumbley, S. Abdallah, 2010 .

Maximizing information about a noisy signal with a single non-linear neuron

Mark D. Plumbley, J. Orwell, 1999 .

Estimating parameters from audio for an EG+LFO model of pitch envelopes

Mark D. Plumbley, Stephen J. Welburn, 2009 .

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

Mark D. Plumbley, Yuxuan Wang, Qiuqiang Kong, 2019, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

Robustness and independence of voice timbre features under live performance acoustic degradations

Mark D. Plumbley, D. Stowell, 2008 .

Tempo estimation and beat tracking with adaptive input selection

Mark D. Plumbley, M. Davies, 2006 .

Audio Source Separation by Time-Frequency Masking using a Signal-Adaptive Local Cosine Transform

Mark D. Plumbley, M. Davies, Andrew Nesbit, 2019 .

INEXACT PROXIMAL OPERATORS FOR `p-QUASINORM MINIMIZATION

Mark D. Plumbley, Cian O’Brien, 2018 .

GEOMETRIC ICA USING NONLINEAR CORRELATION AND MDS

Mark D. Plumbley, S. Abdallah, 2003 .

A SIGNAL-ADAPTIVE LOCAL COSINE TRANSFORM FOR SOURCE SEPARATION BY TIME-FREQUENCY MASKING

Mark D. Plumbley, Andrew Nesbit, 2006 .

Evidence that phrase-level tempo variation may be represented using a limited dictionary

Mark D. Plumbley, E. Chew, Shengchen Li, 2014 .

Sound Event Detection: A tutorial

Mark D. Plumbley, A. Mesaros, T. Virtanen, 2021, IEEE Signal Processing Magazine.

Edinburgh Research Explorer An adaptive stereo basis method for convolutive blind audio source separation

Mark D. Plumbley, M. Davies, E. Vincent, 2007 .

FDICA DUET ASB FDICA DUET ASB FDICA DUET ASB FDICA DUET ASB FDICA DUET ASB FDICA DUET

Mark D. Plumbley, Emmanuel Vincent, Maria G. Jafari, 2006 .

Tempo estimation and beat tracking with adaptive input selection

Mark D. Plumbley, M. Davies, 2006 .

Chapter 1 Introduction to Sound Scene and Event Analysis

Mark D. Plumbley, D. Ellis, T. Virtanen, 2019 .

Sparse Recovery and Dictionary Learning From Nonlinear Compressive Measurements

Mark D. Plumbley, F. Bach, Wenwu Wang, 2018, IEEE Transactions on Signal Processing.

Sparse kernel dictionary learning

Mark D. Plumbley, Mark D. Plumbley, Cian O'Brien, 2016 .

UNTWIST: A NEW TOOLBOX FOR AUDIO SOURCE SEPARATION

Mark D. Plumbley, Emad M. Grais, Gerard Roma, 2016 .

Detection and Classification of Acoustic Scenes and Events

Dan Stowell, Mathieu Lagrange, Mark D. Plumbley, 2015, IEEE Transactions on Multimedia.

Evaluation of live human-computer music-making: Quantitative and qualitative approaches

Dan Stowell, Mark D. Plumbley, Nick Bryan-Kinns, 2009, Int. J. Hum. Comput. Stud..

How Predictable Do We Like Our Music? Eliciting Aesthetic Preferences With The Melody Triangle Mobile App

Mark D. Plumbley, Henrik Ekeus, Samer A. Abdallah, 2013 .

A Track-Wise Ensemble Event Independent Network for Polyphonic Sound Event Localization and Detection

Mark D. Plumbley, Qiuqiang Kong, J. Yang, 2022, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

TWO-STAGE SOUND EVENT LOCALIZATION AND DETECTION USING INTENSITY VECTOR AND GENERALIZED CROSS-CORRELATION Technical Report

Mark D. Plumbley, Qiuqiang Kong, Turab Iqbal, 2019 .

Sound Event Detection of Weakly Labelled Data With CNN-Transformer and Automatic Threshold Optimization

Mark D. Plumbley, Qiuqiang Kong, Yong Xu, 2019, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

Audio for Audio is Better? An Investigation on Transfer Learning Models for Heart Sound Classification

Björn W. Schuller, Mark D. Plumbley, Kun Qian, 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

A prototype system for object coding of musical audio

Mark D. Plumbley, E. Vincent, M.D. Plumbley, 2005, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2005..

Single-Channel Signal Separation and Deconvolution with Generative Adversarial Networks

Mark D. Plumbley, Qiuqiang Kong, Philip J. B. Jackson, 2019, IJCAI.

Source Separation with Weakly Labelled Data: an Approach to Computational Auditory Scene Analysis

Mark D. Plumbley, Yuxuan Wang, Qiuqiang Kong, 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

CL4AC: A Contrastive Loss for Audio Captioning

Mark D. Plumbley, Wenwu Wang, Xubo Liu, 2021, DCASE.

Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio Source Separation

Mark D. Plumbley, Emad M. Grais, H. Wierstorf, 2017, LVA/ICA.

Deep Learning Based Energy Disaggregation and On/Off Detection of Household Appliances pdf

Mark D. Plumbley, Qiuqiang Kong, Nigel Gilbert, 2019, ACM Trans. Knowl. Discov. Data.

Predicting the Perceived Level of Reverberation using Features from Nonlinear Auditory Model

Mark D. Plumbley, Saeid Safavi, Wenwu Wang, 2018 .

Proceedings of the Detection and Classification of Acoustic Scenes and Events 2016 Workshop (DCASE2016)

Daniel P. W. Ellis, Mark D. Plumbley, Gaël Richard, 2016 .

Source Separation of the Second Heart Sound via Alternating Optimization

Mark D. Plumbley, F. Renna, M. Coimbra, 2021, 2021 Computing in Cardiology (CinC).

Hearing the shape of a room

Mark D. Plumbley, Mark D Plumbley, 2013, Proceedings of the National Academy of Sciences.

Separating Musical Audio Signals

Mark D. Plumbley, 2014 .

CAA-Net: Conditional Atrous CNNs With Attention for Explainable Device-Robust Acoustic Scene Classification pdf

Björn W. Schuller, Mark D. Plumbley, Zhao Ren, 2020, IEEE Transactions on Multimedia.

Learning With Out-of-Distribution Data for Audio Classification

Mark D. Plumbley, Qiuqiang Kong, Yin Cao, 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

An adaptive orthogonal sparsifying transform for speech signals

Mark D. Plumbley, M.D. Plumbley, M. Jafari, 2008, 2008 3rd International Symposium on Communications, Control and Signal Processing.

Dictionaries

Mark D. Plumbley, C. West Churchman, D. Barchiesi, 2018, Swift 4.2 Essentials.

Proceedings of the 7th international conference on Independent component analysis and signal separation

Mark D. Plumbley, Mike E. Davies, Samer A. Abdallah, 2007 .

A source localization approach based on structured sparsity for broadband far-field sources

Mark D. Plumbley, Aris Gretsistas, 2011 .

Event-Independent Network for Polyphonic Sound Event Localization and Detection pdf

Mark D. Plumbley, Qiuqiang Kong, Yin Cao, 2020, DCASE.

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

Mark D. Plumbley, Yong Xu, Qiuqiang Kong, 2017, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Sound Event Localization and Detection Using CRNN on Pairs of Microphones pdf

James R. Glass, Mark D. Plumbley, Mark D. Plumbley, 2019, DCASE.

STACKED CONVOLUTIONAL NEURAL NETWORKS FOR GENERAL-PURPOSE AUDIO TAGGING Technical Report

Mark D. Plumbley, Qiuqiang Kong, Turab Iqbal, 2018 .

Delayed Decision-making in Real-time Beatbox Percussion Classification

Dan Stowell, Mark D. Plumbley, Mark D. Plumbley, 2010 .

Audio source separation using sparse representations

Emmanuel Vincent, Mark D. Plumbley, Maria G. Jafari, 2011 .

Theorems on Positive Data: On the Uniqueness of NMF

Mark D. Plumbley, L. K. Hansen, S. H. Jensen, 2008, Comput. Intell. Neurosci..

Score informed audio source separation using constrained nonnegative matrix factorization and score synthesis

Mark D. Plumbley, J. Fritsch, 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

Original Contribution: Efficient information transfer and anti-Hebbian neural networks

Mark D. Plumbley, 1993 .

Efficient Bayesian inference for harmonic models via adaptive posterior factorization

Mark D. Plumbley, E. Vincent, 2008, Neurocomputing.

Model-Based Audio Source Separation

Mark D. Plumbley, S. Abdallah, M. Jafari, 2006 .

Audio Source Separation using Audio Source Separation using Audio Source Separation using Audio Source Separation using Sparse Representations Sparse Representations Sparse Representations Sparse Representations

Mark D. Plumbley, M. Jafari, Andrew Nesbit, 2010 .

Using UMAP to Inspect Audio Data for Unsupervised Anomaly Detection under Domain-Shift Conditions

Mark D. Plumbley, A. Fernández, 2021, DCASE.

Latent Variable Analysis and Signal Separation

Mark D. Plumbley, S. Gannot, R. Mason, 2018, Lecture Notes in Computer Science.

An Improved Event-Independent Network for Polyphonic Sound Event Localization and Detection

Mark D. Plumbley, Qiuqiang Kong, Wenwu Wang, 2020, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).