Comments (3)
andimarafioti
commented on July 17, 2024
It shouldn't be a problem if you're generating magnitude spectrograms (as on the paper accompanying this repository). If you're working with phase, this problem has previously been reported for convolutional networks (see [1]). On [2] they generate phases using GANs and don't discuss this, but Figure 3 might give you some insight on it.
[1] https://www.researchgate.net/publication/328600925_A_context_encoder_for_audio_inpainting
[2] https://openreview.net/pdf?id=H1xQVn09FX
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jorge-loop
commented on July 17, 2024
I am only interesting in reconstructing the magnitude. I have noticed that
while the generated data are really good and visually seems close to the
real ones, however, it seems that i am loosing quality mainly from the
high-frequencies (since the generated spectrograms having less red colour).
Therefore, I am trying to figure out if it is a common issue when
generating spectrograms.
…On Wed, 9 Jan 2019 at 18:12, Andrés Marafioti ***@***.***> wrote:
It shouldn't be a problem if you're generating magnitude spectrograms (as
on the paper accompanying this repository). If you're working with phase,
this problem has previously been reported for convolutional networks (see
[1]). On [2] they generate phases using GANs and don't discuss this, but
Figure 3 might give you some insight on it.
[1]
https://www.researchgate.net/publication/328600925_A_context_encoder_for_audio_inpainting
[2] https://openreview.net/pdf?id=H1xQVn09FX
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chrisdonahue
commented on July 17, 2024
Not entirely sure. I speculate that the problem is two-fold and has to do with both the GAN generation and the Griffin-Lim reconstruction. Not clear what proportion of the high frequencies issues can be attributed to each component.
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