You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Dear Professor Zhu,I'm a beginner, so I'm sorry to bother you if I ask some stupid questions; please bear with me and forgive me.
I encountered some problems when using the project:
The training data I use is very long, for example, 1800 seconds, the itp is 600 seconds, its value is 60000; and the DeepDenoiser original plan comes with 30s. Do I need to change a lot of parameters?
(For example, nperseg nfft nt X_shape, are there any principles for selecting these parameters?)
Because my data length is long ,so do neural network parameters need to be selected? For example, the size of the convolution kernel.
The above is my question, and I sincerely look forward to your reply;
Thank you so much.
Best Regards
The text was updated successfully, but these errors were encountered:
zhoudongJenny
changed the title
About train data : time lenth ,;and some code parameters : such as STFT window parameters and so on
About train data : time lenth ; and some code parameters : such as STFT window parameters and so on
Jan 15, 2024
Dear Professor Zhu,I'm a beginner, so I'm sorry to bother you if I ask some stupid questions; please bear with me and forgive me.
I encountered some problems when using the project:
The training data I use is very long, for example, 1800 seconds, the itp is 600 seconds, its value is 60000; and the DeepDenoiser original plan comes with 30s. Do I need to change a lot of parameters?
(For example, nperseg nfft nt X_shape, are there any principles for selecting these parameters?)
Because my data length is long ,so do neural network parameters need to be selected? For example, the size of the convolution kernel.
The above is my question, and I sincerely look forward to your reply;
Thank you so much.
Best Regards
The text was updated successfully, but these errors were encountered: