Research

Most of my current research is in the area of Independent Component Analysis (ICA) and related techniques, applied to audio and music signals. Specific research areas include: Analysis and transcription of musical audio signals using ICA and related techniques; Object-based coding of audio; Rhythm tracking and automatic accompaniment; Links between ICA and efficient information transmission; and Non-negative ICA. This work takes place in the Centre for Digital Music, within the Digital Signal Processing & Multimedia Group.

Blind Source Separation and Independent Component Analysis

My research is in the area of signal processing known as blind source separation (BSS) using independent component analysis (ICA). Sometimes known as the "cocktail party problem", the task is to recover original source signals from observations of mixtures. This problem is found many real-world signals, and these methods are finding increasing application in the analysis of e.g. EEG signals, audio, satellite images, and text documents. In the Centre for Digital Music I am investigating theory and algorithms for BSS and ICA, and applying these to the analysis of musical audio signals. This has proved to be a very exciting and fruitful area: its importance is indicated for example by the widespread interest in our EPSRC-funded ICA Research Network (initially with 44 researchers in 26 UK institutions), and success of the recent international ICA conferences.

I have developed a Non-negative ICA approach to separation of positive sources (Plumbley, 2001): many real world tasks (including music spectra) share these non-negative features. I have proved sufficient conditions for algorithms to find this non-negative ICA solution (Plumbley, 2002), and developed and analyzed novel algorithms for non-negative ICA with Prof. Erkki Oja (Plumbley, 2003; Plumbley & Oja, 2004). This work led me to investigate the geometrical structure of the algorithm search space (constrained to orthonormal matrices) in terms of a Lie group structure. Using this geometric view gives a deep insight into methods for non-negative ICA and “standard” ICA methods, allowing faster searching (Plumbley 2005 [Neurocomputing]).

• M. G. Jafari, S. A. Abdallah, M. D. Plumbley and M. E. Davies. Sparse coding for convolutive blind audio source separation. To appear in Proceedings of the 6th International Conference on Independent Component Analysis and Blind Source Separation (ICA 2006), Charleston, SC, USA, 5-8 March 2006.
• Y. Nishimori, S. Akaho and M. D. Plumbley. Riemannian Optimization Method on the Flag Manifold for Independent Subspace Analysis. To appear in Proceedings of the 6th International Conference on Independent Component Analysis and Blind Source Separation (ICA 2006), Charleston, SC, USA, 5-8 March 2006.
• M. D. Plumbley. Geometrical Methods for Non-negative ICA: Manifolds, Lie Groups and Toral Subalgebras. Neurocomputing, 67, 161-197, 2005.
• E. Vincent, M. G. Jafari, S. A. Abdallah, M. D. Plumbley and M. E. Davies. Blind Audio Source Separation. Technical Report C4DM-TR-05-01. Centre for Digital Music, Queen Mary University of London, 24 November 2005.
• B. Wang and M. D. Plumbley. Musical audio stream separation by non-negative matrix factorization. In Proceedings of the DMRN Summer Conference, Glasgow, 23-24 July 2005.
• M. D. Plumbley. Lie Group Methods for Optimization with Orthogonality Constraints. In Proceedings of the International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2004), pp 1245-1252, Granada, Spain, September 22-24, 2004. [Invited Keynote]
• E. Oja and M. D. Plumbley. Blind Separation of Positive Sources by Globally Convergent Gradient Search. Neural Computation 16(9), pp 1811-1825, Sept 2004.
• M. D. Plumbley and E. Oja. A 'Non-Negative PCA' Algorithm for Independent Component Analysis. IEEE Transactions on Neural Networks, 15(1), pp 66-76, Jan 2004.
• M. D. Plumbley. Algorithms for non-negative independent component analysis. IEEE Transactions on Neural Networks, 14 (3), pp 534- 543, May 2003.
• M. D. Plumbley. Conditions for non-negative independent component analysis. IEEE Signal Processing Letters, 9(6) , pp177 -180, June 2002.
• M. D. Plumbley. Adaptive Lateral Inhibition for Non-negative ICA. In Proceedings of the International Conference on Independent Component Analysis and Blind Signal Separation (ICA2001), San Diego, California, December 9-13, 2001. Pages 516-521, 2001.

Automatic Music Transcription and Sparse Coding of Music

I have investigated the application of ICA, and the related method of sparse coding, to the challenging task of automatic music transcription, the extraction of notes played by a musical instrument from a musical audio recording. Sparse coding, where it is assumed that few sources are active at any time, is good model for many real-world problems. In music, for example, few notes are assumed to be on at any one time. On my EPSRC grant “Automatic Polyphonic Music Transcription Using Multiple Cause Models and Independent Component Analysis” we developed a novel non-negative sparse coding method to perform this transcription, in an unsupervised manner, without prior information about notes spectra (Abdallah & Plumbley, 2005). One of the Assessors of the Final Report of this grant considered the demonstrated results to be “nothing short of amazing” and that “it is a major step forward for this technology”. The overall assessment of this project by ESPRC was “Outstanding”.

• S. A. Abdallah and M. D. Plumbley. Unsupervised analysis of polyphonic music using sparse coding in a probabilistic framework. To appear in IEEE Transactions on Neural Networks.
• M. D. Plumbley, S. A. Abdallah, T. Blumensath and M. E. Davies. Sparse Representations of Polyphonic Music. To appear in Signal Processing.
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• S. A. Abdallah and M. D. Plumbley. Polyphonic transcription by non-negative sparse coding of power spectra. In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 318-325, Barcelona, Spain, October 10-14, 2004.
• S. A. Abdallah and M. D. Plumbley. An Independent Component Analysis approach to Automatic Music Transcription. In Proceedings of the 114th AES Convention, Amsterdam, March 2003.
• S. A. Abdallah and M. D. Plumbley. Probability as metadata: Event detection in music using ICA as a conditional density model. In Proceedings of the Fourth International Symposium on Independent Component Analysis (ICA2003), pp 233-238, Nara, Japan, April 1-4, 2003.
• S. A. Abdallah and M. D. Plumbley. Unsupervised onset detection: a probabilistic approach using ICA and and a hidden Markov classifier. In Proceedings of the Cambridge Music Processing Colloquium, Cambridge, UK, March 2003.
• M. D. Plumbley, S. A. Abdallah, J. P. Bello, M. E. Davies, G. Monti and M. B. Sandler. Automatic music transcription and audio source separation. Cybernetics and Systems, 33(6), pp603-627, 2002. [article][figures]

Beat Tracking and Dynamics of Musical Audio

I am currently investigating methods to analyze the temporal structure of music, such as onset detection and beat tracking, with one of my PhD students, Matthew Davies (Davies & Plumbley 2004, 2005), and we will undertake a fundamental investigation of the temporal structure of musical signals on the recently awarded “Information Dynamics of Music” EPSRC project. Reviewers commented that “this kind of research is badly needed” and is of “fundamental importance”.

• M. E. P. Davies and M. D. Plumbley. Comparing mid-level representations for audio based beat tracking. In Proceedings of the DMRN Summer Conference, Glasgow, 23-24 July 2005.
• M. E. P. Davies, P. Brossier and M. D. Plumbley. Beat Tracking Towards Automatic Musical Accompaniment. In Proceedings of the AES 118th Convention, Barcelona, May 28-31, 2005.
• M. E. P. Davies and M. D. Plumbley. Beat Tracking With A Two State Model. To appear in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005), Philadelphia, USA, March 19-23, 2005
• M. E. P. Davies and M. D. Plumbley. Causal Tempo Tracking of Audio. In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 164-169, Barcelona, Spain, October 10-14, 2004.
• S. A. Abdallah and M. D. Plumbley. Probability as metadata: Event detection in music using ICA as a conditional density model. In Proceedings of the Fourth International Symposium on Independent Component Analysis (ICA2003), pp 233-238, Nara, Japan, April 1-4, 2003.

Object-Based Coding of Musical Audio

I am building on the music analysis and transcription work with Emmanuel Vincent on the EPSRC grant on Object-Based Coding of Musical Audio, following on from work of my PhD student Paul Brossier. Here we aim to use the analysis, transcription and modelling of musical audio to produce compact and scalable “object-based” representations of music which could be used to transmit music at very low bit rates, much lower than the ubiquitous “MP3” format, for example. As recognized by one of the grant reviewers, this project “tackles crucial and as yet unresolved issues” in audio coding.

• E. Vincent and M. D. Plumbley. A prototype system for object coding of musical audio. In Proceedings of the 2005 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 05), 2005.
• E. Vincent and M. D. Plumbley. Predominant-F0 estimation using Bayesian harmonic waveform models. In Proceedings of the 1st Annual Music Information Retrieval Evaluation eXchange (MIREX 2005), Audio Melody Extraction contest, September 2005.
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• P. Brossier, J. P. Bello and M. D. Plumbley. Real-time temporal segmentation of note objects in music signals. In Proceedings of the International Computer Music Conference (ICMC 2004), Florida, USA, November 1-6, 2004.
• P. Brossier, J. P. Bello and M. D. Plumbley. Fast labelling of notes in music signals. In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 331-336, Barcelona, Spain, October 10-14, 2004.
• P. Brossier, M. B. Sandler and M. D. Plumbley. Matching Live Sources with Physical Models. In Proceedings of the 6th International Conference on Digital Audio Effects (DAFx-03), pp 305-307, 8-11 Sept, 2003.
• P. Brossier, M. B. Sandler and M. D. Plumbley. Real-time object-based coding. In Proceedings of the 114th AES Convention, Amsterdam, March 2003.

Recent Research Funding

Current

• Sparse Representations for Signal Processing and Coding (EPSRC Grant EP/D000246/1, £282,902, 2005-2008), with Mike Davies.
• Techniques and Algorithms for Understanding the Information Dynamics of Music (EPSRC Grant GR/S82213/01, £184,965 to Queen Mary, 2005-2007), with Mark Sandler at Queen Mary, Geraint Wiggins and Michael Casey at Goldsmiths College, London.
• ICA Research Network, ICArn.org [Blind Source Separation and Independent Component Analysis Network] (EPSRC Grant EP/C005554/1, £62,058, 2005-2007), with Mike Davies at Queen Mary, plus 44 other researchers at 26 other UK institutions.
• Object-based Coding of Musical Audio (EPSRC Grant GR/S75802/01, £202,353 , 2004-2007), with Mark Sandler and Mike Davies.
• Advanced Subband Systems for Audio Source Separation (EPSRC Grant GR/S85900/01, £176,431, 2004-2007), with Mike Davies and Mark Sandler.
• SIMAC: Semantic Interaction with Music Audio Contents (EU Grant FP6-IST-507142, €2,982,921 total, £219,000 to Queen Mary, 2004-2006), with Mark Sandler at Queen Mary. Partners: UPF (Spain), ÖFAI (Austria), Matrix Data (UK), Philips Research (Netherlands).
• Digital Music Research Network [dmrn.org]. (EPSRC Grant GR/R64810/01, "Music Processing Network" £61,794, 2003-2005), with Mike Davies and Mark Sandler at Queen Mary, plus researchers at six other UK institutions.

Completed

• Estimation of Melody from Audio using Harmonic and Rhythmic Information (Royal Society International Incoming Short Visit, £3,253, Oct-Nov 2005) with Juan Bello at Queen Mary and Dr Matija Marolt, Univ of Ljubljana, Slovenia.
• Automatic Polyphonic Music Transcription Using Multiple Cause Models and Independent Component Analysis (EPSRC Grant GR/R54620/01, £124,946, 2002-2004), with Mike Davies and Mark Sandler. EPSRC Final Assessment: Outstanding.
• Information and noise in ICA for music (Leverhulme Trust Study Abroad Fellowship RF&G/10214: £12,020, April-July 2002). Three month visit to Helsinki University of Technology.