hey,

You can find links to talks he's given - such a kind gesture given the lack
of free resources and machine learning applied to audio compared to other
topics. From a lecture recording, it appears the algorithm comprises of
deep neural network with a mask approach. This has been used to improve
hearing aids too for example and as far as I know, believing in izotope's
blog post, this is how izotope RX does the de-rustle for microphone noise.

I'd love to hear if others have been using DNN for audio, I am a bit more
interested in DNN processing audio (ie, outputs processed audio) than
classic classification approaches where people are mostly borrowing ideas
from computer vision and classifying based on spectrogram representations
(think SFFT).

Cheers
b


On Fri, Sep 6, 2019 at 9:10 AM Andrew Luke Nesbit <em...@andrewnesbit.org>
wrote:

> Yes, indeed.  I would also love to learn more about this.  I worked on
> audio source separation for my PhD and postdoctoral research some years
> ago.
>
> The webpage says to write to Roger directly ("For more info, please
> contact Roger Jang.")
>
> Andrew
>
> On 06/09/2019 13:42, Patric Schmitz wrote:
> > Impressive! Is the algorithm published and/or is it implemented in your
> > Speech & Audio Processing Toolbox?
> >
> > Thank you,
> > Patric
> >
> > On 9/6/19 8:21 AM, Jyh-Shing Roger Jang wrote:
> >> Dear community:
> >> (Sorry for cross posting, if any.)
> >> I???d like to bring your attention to our recent demo system for
> >> singing voice separation:
> >> http://mirlab.org/demo/svs
> >> You can simply input a youtube link and the system can separate the
> >> vocal from background music. Feel free to let me know if you have any
> >> questions.
> >> J.-S. Roger Jang (?????????)
> >> Prof, CSIE Dept, National Taiwan Univ, Taiwan (http://mirlab.org/jang)
> >>
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