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about the unet model #48

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ucasiggcas opened this issue Sep 23, 2019 · 7 comments
Open

about the unet model #48

ucasiggcas opened this issue Sep 23, 2019 · 7 comments

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@ucasiggcas
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hi,
who has got success by using the unet model in model_unet.py and train_unet.py,
any one could help me to get the unet model,sorry, it's a little difficult to understand the ##
how to get the unet version ?
And the unet need the RNN ???

who can give me the script in py ?
thx

@ucasiggcas
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who could translate the model in keras???

@ucasiggcas
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#49

@jadujoel
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jadujoel commented Oct 1, 2019 via email

@ucasiggcas
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Here's a unet model in keras style: https://www.dropbox.com/sh/v2rd5fyx1vozvmb/AACuSCNvxozcmx1Z-i4fE6Gka?dl=0 https://www.dropbox.com/sh/v2rd5fyx1vozvmb/AACuSCNvxozcmx1Z-i4fE6Gka?dl=0 Model config is inspired by https://ismir2017.smcnus.org/wp-content/uploads/2017/10/171_Paper.pdf https://ismir2017.smcnus.org/wp-content/uploads/2017/10/171_Paper.pdf Hope that helps. Best / Joel

On 1 Oct 2019, at 04:29, Tychee @.***> wrote: who could translate the model in keras??? — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#48?email_source=notifications&email_token=AFPXR7EUUUMWD47QT3VSPBDQMKYYTA5CNFSM4IZHCG4KYY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOD77WS7I#issuecomment-536832381>, or mute the thread https://github.com/notifications/unsubscribe-auth/AFPXR7E5EF2UFKYPO4SOVQLQMKYYTANCNFSM4IZHCG4A.

thx
will have a try.

@ucasiggcas
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ucasiggcas commented Oct 8, 2019

Dear
if you use the keras RNN ,will be great,for the UNET model is big.
maybe 3 layers RNN and 2 dense, 2 mask layers will be the best model
@jadujoel

and how to get the mask by keras ?

@ucasiggcas
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ucasiggcas commented Oct 16, 2019

@jadujoel
Dear,
I find that in your data.py,maybe something is wrong?

    def make_dataset(self):
        self.load_audio()
        X_ffts = self.make_ffts(self.X_signals)
        X_mags = np.abs(X_ffts)
        self.X, self.X_max = self.normalize(X_mags)

        Y_ffts = self.make_ffts(self.Y_signals)
        Y_mags = np.abs(X_ffts)
        self.Y, self.Y_max = self.normalize(X_mags)

self.Y, self.Y_max = self.normalize(X_mags)

R U sure ?

@ucasiggcas
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ucasiggcas commented Oct 16, 2019

And another question,
fft = librosa.stft(signal, n_fft=1024, hop_length=512)[1:,:]

if U only wanna the 1:513, how to restore the signal ?

x=X_signals[0]
xstft=librosa.stft(x, n_fft=1024, hop_length=512)[1:,:]
xistft=librosa.istft(xstft,win_length=1024,hop_length=512)

it's not right!!

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