| Deep karaoke: Extracting vocals from musical mixtures using a convolutional deep neural network AJR Simpson, G Roma, MD Plumbley International Conference on Latent Variable Analysis and Signal Separation …, 2015 | 165 | 2015 |
| Self-driving car steering angle prediction based on image recognition S Du, H Guo, A Simpson arXiv preprint arXiv:1912.05440, 2019 | 125 | 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 | 59 | 2017 |
| Single channel audio source separation using deep neural network ensembles EM Grais, G Roma, AJR Simpson, M Plumbley AES Convention Proceedings, 2016 | 50 | 2016 |
| Probabilistic binary-mask cocktail-party source separation in a convolutional deep neural network AJR Simpson arXiv preprint arXiv:1503.06962, 2015 | 39 | 2015 |
| The mathematics of mixing M Terrell, A Simpson, M Sandler Journal of the audio engineering society 62 (1/2), 4-13, 2014 | 37 | 2014 |
| Combining mask estimates for single channel audio source separation using deep neural networks EM Grais, G Roma, AJR Simpson, M Plumbley Interspeech2016 Proceedings, 2016 | 31 | 2016 |
| Abstract learning via demodulation in a deep neural network AJR Simpson arXiv preprint arXiv:1502.04042, 2015 | 31 | 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 | 31 | 2014 |
| Syncopation and the score C Song, AJR Simpson, CA Harte, MT Pearce, MB Sandler PLoS One 8 (9), e74692, 2013 | 27 | 2013 |
| Over-sampling in a deep neural network AJR Simpson arXiv preprint arXiv:1502.03648, 2015 | 24 | 2015 |
| 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 | 23 | 2014 |
| Discriminative enhancement for single channel audio source separation using deep neural networks EM Grais, G Roma, AJR Simpson, MD Plumbley international conference on latent variable analysis and signal separation …, 2017 | 20 | 2017 |
| 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 | 15 | 2013 |
| 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 |
| Time-frequency trade-offs for audio source separation with binary masks AJR Simpson arXiv preprint arXiv:1504.07372, 2015 | 14 | 2015 |
| 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 | 13 | 2015 |
| 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, 2016 | 12 | 2016 |
| Dither is better than dropout for regularising deep neural networks AJR Simpson arXiv preprint arXiv:1508.04826, 2015 | 12 | 2015 |
| Tuning of human modulation filters is carrier-frequency dependent AJR Simpson, JD Reiss, D McAlpine PLoS One 8 (8), e73590, 2013 | 12 | 2013 |
