| 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 | 164 | 2015 |
| Self-driving car steering angle prediction based on image recognition S Du, H Guo, A Simpson arXiv preprint arXiv:1912.05440, 2019 | 103 | 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 | 57 | 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 | 34 | 2014 |
| 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 |
| Probabilistic binary-mask cocktail-party source separation in a convolutional deep neural network AJR Simpson arXiv preprint arXiv:1503.06962, 2015 | 32 | 2015 |
| 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 | 23 | 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 | 13 | 2013 |
| Dither is better than dropout for regularising deep neural networks AJR Simpson arXiv preprint arXiv:1508.04826, 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 |
| Auditory scene analysis and sonified visual images. Does consonance negatively impact on object formation when using complex sonified stimuli? DJ Brown, AJR Simpson, MJ Proulx Frontiers in psychology 6, 1522, 2015 | 11 | 2015 |
