Hi Looks like this just extrapolates the upper part of the spectrum, and not super well judging by those images. codec2 already tries to code the harmonic content via vector quantization, so isn't really low- samplerate in the same way. In fact, I think David is working on getting 16+ ksps working. If done right then the decoder can "invent" contents for the 4-8 kHz band even if the original signal was 0-4 kHz (8 ksps).
I'm somewhat skeptical of applying multi-layer neural networks ("deep
learning") to everything, since you don't really understand what the
hell it's doing. Plus there's always the risk of overfitting. But this
doesn't mean you couldn't do good work with them, especially if the
input has been appropriately preprocessed. Something like an
autoencoder[1] could be useful as part of a whole class of codecs. I
experimented with this in the early 2000's. Another idea is to do some
kind of temporal thing; codec2 has almost no memory at the moment I
think.
There's a corpus of speech samples used to train the vector quantizer,
perhaps you could experiment on that? Turn that inspiration into
perspiration!
/Tomas
[1] https://en.wikipedia.org/wiki/Autoencoder
On Fri, 2017-06-23 at 20:11 -0300, Ricardo Andere de Mello wrote:
> Hi,
>
> Recently I have been working with deep learning, but mainly focused
> in image recognition.
>
> I found one article related to audio, and I thought you would find it
> interesting:
>
> https://blog.insightdatascience.com/using-deep-learning-to-reconstruc
> t-high-resolution-audio-29deee8b7ccd
>
> I was wondering if it would be possible to use codec2 frames as
> inputs for training.
>
> []s, Ricardo Mello
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