I am a lecturer applying machine learning to sound. I develop new techniques in structured "machine listening", using both machine learning and signal processing to analyse soundscapes with multiple birds. I have also worked on voice, music, birdsong and environmental soundscapes. More detail on my personal research page.
2019 and forthcoming
D. Stowell, T. Petrusková, M. Šálek, P. Linhart, Automatic acoustic identification of individual animals: Improving generalisation across species and recording conditions, Journal of the Royal Society Interface 16 (153), article 20180940, 2019.
W.J. Wilkinson, M. Riis Andersen, J.D. Reiss, D. Stowell, A. Solin, End-to-End Probabilistic Inference for Nonstationary Audio Analysis. In Proceedings of the International Conference on Machine Learning (ICML 2019), 2019.
W.J. Wilkinson, M. Riis Andersen, J.D. Reiss, D. Stowell, A. Solin, Unifying Probabilistic Models for Time-Frequency Analysis. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2019), 3352-3356, 2019.
P.A. Alvarado, M.A. Álvarez, D. Stowell, Sparse Gaussian process Audio Source Separation Using Spectrum Priors in the Time-Domain. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2019), 995-999, 2019.
D.F. Yela, D. Stowell, M. Sandler. Spectral Visibility Graphs: Application to Similarity of Harmonic Signals. In European Signal Processing Conference (EUSIPCO) 2019, accepted.
A. Matt, D. Stowell, Estimating & Mitigating the Impact of Acoustic Environments on Machine-to-Machine Signalling. In European Signal Processing Conference (EUSIPCO) 2019.
V. Morfi, Y. Bas, H. Pamuła, H. Glotin and D. Stowell, NIPS4Bplus: a richly annotated birdsong audio dataset, in press.
D.F. Yela, F. Thalmann, V. Nicosia, D. Stowell, M. Sandler. Efficient On-line Computation of Visibility Graphs, submitted.
D. Stowell, Y. Stylianou, M. Wood, H. Pamuła, H. Glotin, Automatic acoustic detection of birds through deep learning: the first Bird Audio Detection challenge, Methods in Ecology and Evolution, 2018.
V. Morfi, D.Stowell, Deep Learning for Audio Transcription on Low-Resource Datasets, Applied Sciences 8(8), article 1397, 2018.
V. Morfi, D. Stowell, Data-Efficient Weakly Supervised Learning for Low-Resource Audio Event Detection Using Deep Learning, in Proceedings of the 2018 DCASE workshop, 2018.
S. McDonald, D. Stowell and N Bryan-Kinns, A Networked Communal Plant Watering System, 2018.
D. Fano Yela, D. Stowell and M. Sandler, Does k Matter? k-NN Hubness Analysis for Kernel Additive Modelling Vocal Separation. Proceedings of LVA-ICA, 2018.
W. J. Wilkinson, J. D. Reiss, D. Stowell, A Generative Model for Natural Sounds Based on Latent Force Modelling. Proceedings of LVA-ICA, 2018.
D. Stowell, E. Benetos, and L. F. Gill, On-bird sound recordings: Automatic acoustic recognition of activities and contexts. IEEE/ACM Trans. on Audio Speech and Language Processing, 25(6), 1193-1206, 2017. [preprint]
D. Stowell. Computational Bioacoustic Scene Analysis. In Computational Analysis of Sound Scenes and Events, T. Virtanen, M. D. Plumbley, and D. P. W. Ellis (eds.), Springer, Oct. 2017.
E. Benetos, D. Stowell, and M. D. Plumbley. Approaches to complex sound scene analysis. In Computational Analysis of Sound Scenes and Events, T. Virtanen, M. D. Plumbley, and D. P. W. Ellis (eds.), Springer, Oct. 2017.
H. Pamula et al, Adaptation of deep learning methods to nocturnal bird audio monitoring, in LXIV Open Seminar on Acoustics (OSA) 2017, Piekary ÅšlÄ…skie, Poland. 2017.
W. Wilkinson, J. Reiss and D. Stowell, Latent force models for sound: Learning modal synthesis parameters and excitation functions from audio recordings. In Proc DAFX 2017, 2017.
V. Morfi and D. Stowell. Deductive refinement of species labelling in weakly labelled birdsong recordings. In Proc ICASSP 2017, 2017. [preprint]
D. Stowell, L. F. Gill, and D. Clayton. Detailed temporal structure of communication networks in groups of songbirds. Journal of the Royal Society Interface, 13(119), 2016. [preprint]
D. Stowell, M. Wood, Ya. Stylianou, and H. Glotin. Bird detection in audio: a survey and a challenge. In Proceedings of MLSP 2016. 2016. [preprint]
D. Stowell, V. Morfi, and L. F. Gill. Individual identity in songbirds: signal representations and metric learning for locating the information in complex corvid calls. In Proceedings of InterSpeech 2016. 2016. [preprint]
P. A. Alvarado and D. Stowell. Gaussian processes for music audio modelling and content analysis. In Proceedings of MLSP 2016. 2016. [preprint]
D. Stowell. Bird Audio Detection challenge 2016: public data. 2016. [dataset]
D. Stowell, D. Giannoulis, E. Benetos, M. Lagrange and M. D. Plumbley, Detection and Classification of Audio Scenes and Events. IEEE Transactions on Multimedia 17(10), 1733-1746, 2015.
D. Stowell and D. Clayton, Acoustic event detection for multiple overlapping similar sources, Proceedings of WASPAA 2015, 2015. [preprint]
D. Stowell, BirdCLEF 2015 submission: Unsupervised feature learning from audio, CLEF 2015 Labs and Workshops, Notebook Papers, 2015.
D. Barchiesi, D. Giannoulis, D. Stowell, and M. D. Plumbley, Acoustic scene classification: Classifying environments from the sounds they produce. IEEE Signal Processing Magazine, 32(3):16--34, 2015. [preprint]
D. Stowell and R. E Turner. Denoising without access to clean data using a partitioned autoencoder. Technical report, 2015.
D. Stowell, Renewal processes and semi-Markov processes in animal vocalisations: A response to Kershenbaum et al. (eLetter). Proceedings of the Royal Society B 281, 2014.
D. Stowell and M. D. Plumbley, Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning. PeerJ 2:e488, 2014.
D. Stowell and M. D. Plumbley, Large-scale analysis of frequency modulation in birdsong databases. Methods in Ecology and Evolution, 2014.
D. Stowell and M. D. Plumbley, An open dataset for research on audio field recording archives: freefield1010. In: Proceedings of the AES Conference on Semantic Audio Conference (AES53), January 2014. [Data]
—Winner: SoundSoftware award for "Reproducibility-Enabling Work"
D. Stowell, Case study: How OpenStreetMap used humans and machines to map affected areas after Typhoon Haiyan. In: Silverman (ed.) The Verification Handbook. Maastricht, The Netherlands: European Journalism Centre, 2014.
D. Stowell and M. D. Plumbley, Feature design for multilabel bird song classification in noise (NIPS4B challenge). Technical note, NIPS4B 2013 Bird Challenge.
D. Stowell and M. D. Plumbley, Segregating event streams and noise with a Markov renewal process model. Journal of Machine Learning Research 14, 1891-1916, 2013.
Giannoulis, D. and Benetos, E. and Stowell, D. and Rossignol, M. and Lagrange, M. and Plumbley, M. D., Detection and classification of acoustic scenes and events: an IEEE AASP challenge. In: Proceedings of the Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2013.
Giannoulis, D. and Stowell, D. and Benetos, E. and Rossignol, M. and Lagrange, M. and Plumbley, M. D., A database and challenge for acoustic scene classification and event detection. In: Proceedings of the European Signal Processing Conference (EUSIPCO), 2013.
D. Stowell and M. D. Plumbley, Acoustic detection of multiple birds in environmental audio by Matching Pursuit. Technical note, ICML 2013 Bird Challenge.
D. Stowell, S. Musevic, J. Bonada and M. D. Plumbley, Improved multiple birdsong tracking with distribution derivative method and Markov renewal process clustering . International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), May 2013. [Poster]
D. Stowell and S. Dixon, Integration of informal music technologies in secondary school music lessons. British Journal of Music Education, 2013.
D. Stowell and A. McLean, Live music-making: a rich open task requires a rich open interface. In: Holland, S., Wilkie, K., Mulholland, P. and Seago, A. (eds.) Music and Human Computer Interaction, 2013.
D. Stowell and M. D. Plumbley, Multi-target pitch tracking of vibrato sources in noise using the GM-PHD filter. In: Proceedings of Proceedings of the 5th International Workshop on Machine Learning and Music (MML12), July 2012.
D. Stowell and E. Chew, Bayesian MAP estimation of piecewise arcs in tempo time-series. In: Proceedings of CMMR 2012, June 2012.
D. Stowell and M. D. Plumbley, Learning timbre analogies from unlabelled data by multivariate tree regression. Journal of New Music Research, 40 (4), 325-336, 2011. DOI:10.1080/09298215.2011.596938
D. Stowell and S. Dixon, MIR in school? Lessons from ethnographic observation of secondary school music classes. In: Proceedings of ISMIR 2011.
D. Stowell and A. McLean, Live music-making: a rich open task requires a rich open interface. In: Proceedings of the BCS HCI 2011 Workshop - When Words Fail: What can Music Interaction tell us about HCI? July 2011
D. Stowell, Scheduling and composing with Risset eternal accelerando rhythms. International Computer Music Conference (ICMC), June 2011.
D. Stowell, M. Barthet, S. Dixon and M. Sandler. Musicology for the masses: Situating new audio technologies for musicology and music education. In: Proceedings of the Digital Economy All Hands Conference 2011, accepted.
D. Stowell, Writing Unit Generator Plugins, in Wilson, Cottle and Collins (eds.) The SuperCollider Book. Cambridge, MA: MIT Press. 2011.
D. Stowell and M. D. Plumbley, Delayed decision-making in real-time beatbox percussion classification. Journal of New Music Research 39 (3), 203-213, September 2010. DOI:10.1080/09298215.2010.512979
D. Stowell and M. D. Plumbley, Birdsong and C4DM: A survey of UK birdsong and machine recognition for music researchers. Tech. Rep. C4DM-TR-09-12, Centre for Digital Music, Queen Mary University of London, August 2010.
D. Stowell, Making music through real-time voice timbre analysis: machine learning and timbral control. PhD thesis, School of Electronic Engineering and Computer Science, Queen Mary University of London, August 2010.
D. Stowell and M. D. Plumbley, Cross-associating unlabelled timbre distributions to create expressive musical mappings, Workshop on Applications of Pattern Analysis, August 2010.
D. Stowell and M. D. Plumbley, Timbre remapping through a regression-tree technique, Sound and Music Computing conference, July 2010.
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D. Stowell and M. D. Plumbley, Fast multidimensional entropy estimation by k-d partitioning, IEEE Signal Processing Letters 16 (6), 537–540, June 2009. DOI:10.1109/LSP.2009.2017346
D. Stowell, A. Robertson, M. D. Plumbley, and N. Bryan-Kinns, Evaluation of live human-computer music-making: quantitative and qualitative approaches, International Journal of Human-Computer Studies 67 (11), 960-975, November 2009. DOI:10.1016/j.ijhcs.2009.05.007
D. Stowell and M. D. Plumbley, Robustness and independence of voice timbre features under live performance acoustic degradations. 11th Conference on Digital Audio Effects (DAFx '08).
D. Stowell, M. D. Plumbley, and N. Bryan-Kinns, Discourse analysis evaluation method for expressive musical interfaces. New Interfaces for Musical Expression (NIME'08)
[Accompanying data file]
D. Stowell and M. D. Plumbley, Characteristics of the beatboxing vocal style, Tech. Rep. C4DM-TR-08-01, Dept. of Electronic Engineering, Queen Mary, University of London, 2008.
D. Stowell and M. D. Plumbley. Adaptive whitening for improved real-time
audio onset detection. In: Proceedings of the International Computer Music Conference (ICMC'07), Copenhagen, Denmark, pp 312-319, August 2007.
D. Stowell and M. D. Plumbley. Pitch-aware real-time timbral re-mapping. In Proceedings of the Digital Music Research Network Summer Conference, Leeds Metropolitan University, UK, 7-8 July 2007.
D. Stowell. Genetic Algorithms and live evolution. SuperCollider symposium,
Birmingham, July 2006.