| Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network AJR Simpson, G Roma, MD Plumbley Latent Variable Analysis and Signal Separation: 12th International …, 2015 | 168 | 2015 |
| Self-driving car steering angle prediction based on image recognition S Du, H Guo, A Simpson arXiv preprint arXiv:1912.05440, 2019 | 108 | 2019 |
| Two-stage single-channel audio source separation using deep neural networks EM Grais, G Roma, AJR Simpson, MD Plumbley IEEE/ACM Transactions on Audio, Speech, and Language Processing 25 (9), 1773 …, 2017 | 58 | 2017 |
| Single-channel audio source separation using deep neural network ensembles EM Grais, G Roma, AJR Simpson, MD Plumbley Audio Engineering Society Convention 140, 2016 | 47 | 2016 |
| The mathematics of mixing M Terrell, A Simpson, M Sandler Journal of the audio engineering society 62 (1/2), 4-13, 2014 | 36 | 2014 |
| Probabilistic binary-mask cocktail-party source separation in a convolutional deep neural network AJR Simpson arXiv preprint arXiv:1503.06962, 2015 | 35 | 2015 |
| Combining Mask Estimates for Single Channel Audio Source Separation Using Deep Neural Networks. EM Grais, G Roma, AJR Simpson, MD Plumbley INTERSPEECH, 3339-3343, 2016 | 32 | 2016 |
| Abstract learning via demodulation in a deep neural network AJR Simpson arXiv preprint arXiv:1502.04042, 2015 | 32 | 2015 |
| Visual objects in the auditory system in sensory substitution: how much information do we need? DJ Brown, AJR Simpson, MJ Proulx Multisensory Research 27 (5-6), 337-357, 2014 | 28 | 2014 |
| Over-sampling in a deep neural network AJR Simpson arXiv preprint arXiv:1502.03648, 2015 | 24 | 2015 |
| Syncopation and the score C Song, AJR Simpson, CA Harte, MT Pearce, MB Sandler PLoS One 8 (9), e74692, 2013 | 22 | 2013 |
| Selective adaptation to “oddball” sounds by the human auditory system AJR Simpson, NS Harper, JD Reiss, D McAlpine Journal of Neuroscience 34 (5), 1963-1969, 2014 | 20 | 2014 |
| Discriminative enhancement for single channel audio source separation using deep neural networks EM Grais, G Roma, AJR Simpson, MD Plumbley Latent Variable Analysis and Signal Separation: 13th International …, 2017 | 19 | 2017 |
| Evaluation of audio source separation models using hypothesis-driven non-parametric statistical methods AJR Simpson, G Roma, EM Grais, RD Mason, C Hummersone, A Liutkus, ... 2016 24th European Signal Processing Conference (EUSIPCO), 1763-1767, 2016 | 14 | 2016 |
| A practical step-by-step guide to the time-varying loudness model of Moore, Glasberg, and Baer (1997; 2002) AJR Simpson, MJ Terrell, JD Reiss Audio Engineering Society Convention 134, 2013 | 14 | 2013 |
| Dither is better than dropout for regularising deep neural networks AJR Simpson arXiv preprint arXiv:1508.04826, 2015 | 12 | 2015 |
| Time-frequency trade-offs for audio source separation with binary masks AJR Simpson arXiv preprint arXiv:1504.07372, 2015 | 12 | 2015 |
| The dynamic range paradox: a central auditory model of intensity change detection AJR Simpson, JD Reiss PLoS One 8 (2), e57497, 2013 | 12 | 2013 |
| Music remixing and upmixing using source separation G Roma, EM Grais, AJR Simpson, MD Plumbley Proceedings of the 2nd AES Workshop on Intelligent Music Production 13, 2016 | 11 | 2016 |
| Untwist: A new toolbox for audio source separation G Roma, EM Grais, AJ Simpson, I Sobieraj, MD Plumbley Extended abstracts for the late-breaking demo session of the 17th …, 2016 | 11 | 2016 |
