Comments (5)
from sednn.
Hi Qiuqiang,
Glad to here from you.
"
I think this might be too big for your 32 GB memory machine. You can either reduce the size of your data, or an optional way is to mix the speech with the noise on the fly so you will have infinite mixture. "
In that case how do I generate the scalar.p file which is a transformation to be applied on each train and test sample?
Thank you,
Akshaya
from sednn.
from sednn.
Hi Qiuqiang,
" That is, you load both timit and noise data to memory. Then you you write a data generator and randomly mix them on the fly to get a minibatch of data. Then no need to use scalar.p any more. "
As per my understanding scaler.p stores the information about mean and standard deviation of entire training data which is used to standardize the training and test audio file by transforming it to another Euclidean space . It will also be used while we find inference of an unknown audio ( not used in training and test dataset). This was the Enhancement parameter using global variance equalization (alpha bar) mentioned in
Reference paper: GLOBAL VARIANCE EQUALIZATION FOR IMPROVING DEEP NEURAL NETWORK BASED SPEECH ENHANCEMENT
Please let me know if I am missing something.
from sednn.
from sednn.
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