Musical Audio Repurposing using Source Separation (MARuSS) is an EPSRC-funded research project (EP/L027119/1) that aims at developing a new approach to high quality audio repurposing, based on high quality musical audio source separation (see about).

Latest publications

  1. E. M. Grais, H. Wierstorf, D. Ward, and M. D. Plumbley, “Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio Source Separation,” in Latent Variable Analysis and Signal Separation: 14th International Conference, LVA/ICA 2018, Guildford, UK, July 3-5, 2018, Proceedings, Y. Deville, S. Gannot, R. Mason, M. D. Plumbley, and D. Ward, Eds. Springer International Publishing, 2018. {Details}
  2. E. M. Grais and M. D. Plumbley, “Combining Fully Convolutional and Recurrent Neural Networks for Single Channel Audio Source Separation,” in Audio Engineering Society Convention 144, Milan, Italy, 2018. {Details}
  3. D. Ward, H. Wierstorf, R. D. Mason, E. M. Grais, and M. D. Plumbley, “BSS EVAL or PEASS? Predicting the Perception of Singing-Voice Separation,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, 2018. {Details}