#Matlab in Computer Engineering Homework Help

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How do I get the max value within a specified range on my plot?

I am writing a program that can read in audio files of recorded guitar notes in standard tuning (e,B,G,D,A,E), plot the signal and the frequency response, then compare the frequency value from the plot with the needed frequency to be in tune. The way I was doing it, was finding the max value or highest peak from the frequency response plot, then comparing it to a specified value needed for it to be in tune. I hit a problem when I got to the G string, because as you can see from the plot, the highest peak is not the first peak, and that is where the frequency I need lies (since a G3 note has to have a frequency of 196 to be in tune), so my method doesn’t work. How can I get the max y value between the 100 & 300 frequency range, then get the x value related to that max value?%Tuning for G String gG = 196.00; %Fourier Transform of G3 On Guitar [y,Fs] = audioread('3rd_String_G_vbr.mp3'); Nsamps = length(y); t = (1/Fs)*(1:Nsamps); %Prepare time data for plot %Do Fourier Transform y_fft = abs(fft(y)); %Retain Magnitude y_fft = y_fft(1:Nsamps/2); %Discard Half of Points f = Fs*(0:Nsamps/2-1)/Nsamps; %Prepare freq data for plot %Find max y value of the highest peak in the range, then find corresponding x %value [maxYValue, indexAtMaxY] = max(y_fft); xValueAtMaxYValue = f(indexAtMaxY(1));

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Use the sampling frequency to find the locations with which to bound the search — HiLo=[100 300]; % frequency bounds nHiLo=round(HiLo/f); % bounding locations [maxYValue, indexAtMaxY] = max(y_fft(nHiLo(1):nHiLo(2)); indexAtMaxY=indexAtMaxY+nHiLo(1); % offset computation

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[prashantgujjar]

Can I hold 2 batches of dlnetwork gradients and update network parameters in 1 operation?

Due to the limitation of GPU memory, a deeplearning network can't learn like 16 samples in a batch.

So can I compute the gradients for a batch of 8 samples, and update the network gradients with 2 batches' gradients?

If I compute the gradients of a deeplearning network by

[gradients,state,loss] = dlfeval(@modelGradient,dlNet,xTrain,yTrain);

So after 2 batches, I get gradients1, gradients2, state1, state2, loss1, and loss2.

For my instant opinion, I think the total gradients should be the mean of gradients1 and gradients2.

But how can I compute the state values? Is it also the mean of state1 and state2?

ANSWER

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Yes, absolutely, just sum the gradients until your batch size is the size you want, then update the model. The principle is exactly the same is training a model on multiple GPUs (or CPUs).

The State update depends on the State. This example shows you how to aggregate batch norm state. The function aggregateState is of interest here. Instead of using gplus you would just be aggregating over your 'sub'-iterations.

function state = aggregateState(state,factor)    numrows = size(state,1);        for j = 1:numrows        isBatchNormalizationState = state.Parameter(j) =="TrainedMean"...            && state.Parameter(j+1) =="TrainedVariance"...            && state.Layer(j) == state.Layer(j+1);                if isBatchNormalizationState            meanVal = state.Value{j};            varVal = state.Value{j+1};

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