Selected key publications from our lab can be found below. For more publications, see the personal pages of our members.
- 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.
- 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.
- H. Pamula et al, Adaptation of deep learning methods to nocturnal bird audio monitoring, in LXIV Open Seminar on Acoustics (OSA) 2017, Piekary Śląskie, Poland. 2017.
- D. Stowell, E. Benetos, and L. F. Gill. On-bird Sound Recordings: Automatic Acoustic Recognition of Activities and Contexts. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 25(6):1193-1206, 2017. Postprint
- E. Benetos, G. Lafay, M. Lagrange and M. D. Plumbley. Polyphonic Sound Event Tracking using Linear Dynamical Systems. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 25(6):1266-1277, 2017. Postprint
- G. Lafay, M. Lagrange, M. Rossignol, E. Benetos, and A. Roebel. A morphological model for simulating acoustic scenes and its application to sound event detection. IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 24, no. 10, pp. 1854-1864, Oct. 2016. Postprint
- S. Abdallah, E. Benetos, N. Gold, S. Hargreaves, T. Weyde, and D. Wolff. The Digital Music Lab: A Big Data Infrastructure for Digital Musicology. ACM Journal on Computing and Cultural Heritage, vol. 10, no. 1, pp. 2:1-2:21, April 2017. Postprint
- 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.
- S. Sigtia, E. Benetos, S. Dixon. An End-to-End Neural Network for Polyphonic Piano Music Transcription. IEEE/ACM Transactions on Audio, Speech, and Language Processing 24(5): 927-939, 2016. DOI: 10.1109/TASLP.2016.2533858
- 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.
- C. Kereliuk, B.L. Sturm, J. Larsen, Deep Learning and Music Adversaries. IEEE Transactions on Multimedia 17(11): 2059-2071, 2015.
- S. Ewert, M.D. Plumbley, M. Sandler, A dynamic programming variant of non-negative matrix deconvolution for the transcription of struck string instruments. In: Proc Int Conf Acoustics, Speech and Signal Processing (ICASSP), 2015.
- E. Benetos, A. Holzapfel. Automatic transcription of Turkish microtonal music. Journal of the Acoustical Society of America 138(4): 2118-2130, 2015. Postprint
- D. Stowell and M. D. Plumbley, Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning. PeerJ 2:e488, 2014.
- B. L. Sturm, A survey of evaluation in music genre recognition, in Adaptive Multimedia Retrieval: Semantics, Context, and Adaptation (A. Nürnberger, S. Stober, B. Larsen, and M. Detyniecki, eds.), vol. LNCS 8382, pp. 29-66, Oct. 2014.
- B. L. Sturm, A simple method to determine if a music information retrieval system is a “horse”. IEEE Transactions on Multimedia 16(6): 1636-1644, 2014.
- B. L. Sturm, The state of the art ten years after a state of the art: Future research in music information retrieval. Journal of New Music Research 43(2): 147-172, 2014.
- S. Ewert, B. Pardo, M. Muller, M.D. Plumbley, Score-informed source separation for musical audio recordings: An overview. IEEE Signal Processing Magazine 31 (3), 116-124. Postprint
- 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.
