Academic:

  • Improving cochlear implant users music perception @ German Hearing Center
    Music appreciation remains rather poor for many Cochlear Implant (CI) users due to their poor pitch perception. Simple music structures with a clear rhythm/beat are well perceived for CI users. By re-mixing the music it is possible to simplify the signal to make it more suitable for implantees. But the multitrack recordings necessary to generate a re-mix are not always accessible. To overcome this limitation, we proposed using source separation techniques to estimate the multitrack recordings.  We conducted perceptual studies with Non-negative Matrix Factorization -based separations, and we further provided additional results considering Deep Recurrent Neural Networks as a source separation algorithm.
  • Drums Transcription @ IRCAM (Paris)
    We studied new contributions for audio event detection methods using Non-negative Matrix Deconvolution and the Itakura Saito divergence – that improve efficiency and numerical stability, and simplify the generation of target pattern sets. A new approach for handling background sounds was proposed and moreover, a new detection criteria based on estimating the perceptual presence of the target class sources was introduced. Experimental results obtained for drum detection in polyphonic music and drum solos demonstrate the beneficial effects of the proposed extensions.

Non academic:

  • Chord Profiles.
    A SIMPLE framework where ALL binary chord profiles are properly defined – available on GitHub.
  • How big is the smallest pitch difference between 2 consecutive tones that a human listener can detect?
    We answered the previous research question via a perceptual test. We concluded that the smallest pitch difference between 2 consecutive tones that a musician can detect (3.18 MELs) is smaller than the smallest pitch difference perceived by a non-musician (11.41 MELs). Further conclusions are listed here.