Hello Rob, sorry for answering late. Somehow I did not see the notification. The signal comes from audio recordings of very different length and sample rate. I am not really sure whether this very simple way of fingerpinting works at the end or I need a sliding window over time at the end which would also result in a fixed resolution. As this would increase the amount of data and complicate the further prpcessing, I first chose the other way un the hope the result is good enough. At tje end the data shall pre-train an adaptive notch filter to remove (changing) background noise. The filter will run on an low power embedded system. So far this is just a vague idea. I had several applications in the past where I would have required the solution, outside of audio.
Robb Brown <robb.br...@gmail.com> schrieb am Mo., 27. März 2017 23:50: > This seems like a very odd thing to be doing. Downsampling the frequency > spectrum is (in the ideal case) equivalent to decreasing the field of view > of the FFT on the original signal. Why don't you just decrease the size of > your original FFT? That will make the FFT faster, and make your > downsampling operation unnecessary. > > > On Monday, March 27, 2017 at 12:26:37 PM UTC-4, Torsten Knodt wrote: > > Hello, > I am totally new to theano and have a first implementation to reduce the > resolution of the frequency axis of FFT data. My function basically sums > multiple consecutive bins to one, but also handles cases where the target > resolution is no integer multiple of the source resolution. > from math import log2 > from numpy import array,arange,asarray,ceil,clip,empty,floor,trunc, > frombuffer,save > from theano import shared,function > from theano.tensor import matrix,scalar,vector > > def resample(self, input_data, fsi, fso, fdo): > fsi = float(fsi) > fdi = fsi/len(input_data) > output_data = empty(shape=int(ceil(fso/fdo))) > for o in range(len(output_data)): > fmino = o*fdo > fnexto = (o+1)*fdo > _v = vector() > _i = scalar(dtype='int64') > _fdi = scalar() > _fmino = scalar() > _fnexto = scalar() > _f1 = function([_i,_fdi,_fmino,_fnexto,_v],(((_i+1)*_fdi).clip(_fmino, > _fnexto)-(_i*_fdi).clip(_fmino,_fnexto))/_fdi*_v[_i]) > output_data[o] = sum([_f1(i,fdi,fmino,fnexto,input_data) for i in > range(int(clip(floor(fmino/fdi),0,len(input_data)-1)),int(clip(ceil(fnexto > /fdi), 1, len(input_data))))]) > output_data = output_data / output_data.sum() > return output_data > > The background is, that I need fixed size FFT data to train a neuronal > network which shall do some classification of the input. > What would be your recommendations to improve the performance of the > function? Improvements to efficiently handle multiple datasets of different > source frequency resolutions would be of special interest. Currently I only > work on my CPU, but plan to move to GPU later. > input_data is already a numpy array. fsi specifies the maximum input > frequency, while fso specifies the maximum output frequency. fdo specifies > the requested resolution of the output. > > Thanks in Advance > Torsten Knodt > > -- > > --- > You received this message because you are subscribed to a topic in the > Google Groups "theano-users" group. > To unsubscribe from this topic, visit > https://groups.google.com/d/topic/theano-users/cmJB8_0HB-k/unsubscribe. > To unsubscribe from this group and all its topics, send an email to > theano-users+unsubscr...@googlegroups.com. > For more options, visit https://groups.google.com/d/optout. > -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to theano-users+unsubscr...@googlegroups.com. For more options, visit https://groups.google.com/d/optout.
