Selected key publications from our lab:
- 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
- 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.
For more publications, see the personal pages of our members.
