The Effect of Spectrogram Reconstructions on Automatic Music Transcription

Congrats Kin Wai (Raven), on this interesting paper on leveraging spectrogram reconstruction for music transcription, which was accepted at the International Conference on Pattern Recognition (ICPR2020). Read the preprint here.

Cheuk K.W., Luo Y.J., Benetos E., Herremans D.. 2021. The Effect of Spectrogram Reconstructions on Automatic Music Transcription: An Alternative Approach to Improve Transcription Accuracy. Proceedings of the International Conference on Pattern Recognition (ICPR2020).

Audio engineer - job opening (nnAudio)

Our team at Singapore University of Technology and Design (SUTD) is looking for an RA for 6 months to help develop nnAudio. You will be joining our team in music/audio/vision AI supervised by Prof. Dorien Herremans. More information on the music/audio team at dorienherremans.com/team. You will be working on the PyTorch audio processing tool nnAudio developed by Cheuk Kin Wai at our lab.

nnAudio, our on-the-fly GPU spectrogram extraction toolbox published in IEEE Access

Congratulations to Raven for publishing 'nnAudio: An on-the-fly GPU Audio to Spectrogram Conversion Toolbox Using 1D Convolutional Neural Networks', in IEEE Access. nnAudio allows you to calculate spectrograms (linear, log, Mel, CQT) on-the-fly as a layer in PyTorch. This makes the spectrograms finetunable to your task! nnAudio is easy to install with pip, see instructions at https://github.com/KinWaiCheuk/nnAudio

PyTorch GPU based audio processing toolkit: nnAudio

Looking for a tool to extract spectrograms on the fly, integrated as a layer in PyTorch? Look no further than nnAudio, a toolbox developed by PhD student Raven (Cheuk Kin Wai): https://github.com/KinWaiCheuk/nnAudio

nnAudio is available in pip (pip install nnaudio), full documentation available on the github page. Also check out our dedicated paper: