frequency

I'm investigating aurioTouch2 sample code. And I noticed, that when we analize audio data, we use only the first buffer of this data, and never other buffers. in void FFTBufferManager::GrabAudioData(AudioBufferList *inBL) function: UInt32 bytesToCopy = min(inBL->mBuffers[0].mDataByteSize, mAudioBufferSize - mAudioBufferCurrentIndex * sizeof(Float32)); memcpy(mAudioBuffer+mAudioBufferCurrentIndex, inBL->mBuffers[0].mData, bytesToCopy); in function static OSStatus PerformThru( void *inRefCon, AudioUnitRenderActionFlags *ioActionFlags, const AudioTimeStamp *inTimeStamp, UInt32 inBusNumber, UInt32

This question already has answers here : Is there an equivalent in ggplot to the varwidth option in plot? (2 answers) Closed 5 years ago . I have a boxplot that I made in R with ggplot2 analagous to the sample boxplot below. The problem is, for the values on the y axis (in this sample, the number of cylinders in the car) I have very different frequencies -- I may have included 2 8 cylinder cars, but 200 4 cylinder cars. Because of this, I'd like to be able to resize the boxplots (in this case, change the height along the y axis) so that the 4 cylinder boxplot is a larger portion of the chart

Right, well. First time I'm actually using Java to fix a problem. I bought a new headphone set called Sennheiser 120 HD; but there's an issue. If there isn't a constant emission of audio then the base for the headphones will eventually time out and turn off. The headphones are spammed with static, which is horrible on the ears. The solution to this for me currently is playing music 24/7 to prevent the static of death. Maybe I'm weird, but I don't want to listen to music 24/7. I believe a workable solution for this would be to constantly emit a sound that the base can detect but I can't hear.

Find the nth most frequent number in array. (There is no limit on the range of the numbers) I think we can (i) store the occurence of every element using maps in C++ (ii) build a Max-heap in linear time of the occurences(or frequence) of element and then extract upto the N-th element, Each extraction takes log(n) time to heapify. (iii) we will get the frequency of the N-th most frequent number (iv) then we can linear search through the hash to find the element having this frequency. Time - O(NlogN) Space - O(N) Is there any better method ? Your method is basically right. You would avoid final

I'm trying to analyze frequency detection algorithms on iOS platform. So I found several implementations using FFT and CoreAudio ( example 1 and example 2 ). But in both cases there is some imprecision in frequency exists: (1) For A4 (440Hz) shows 441.430664 Hz. (1) For C6 (1046.5 Hz) shows 1518.09082 Hz. (2) For A4 (440Hz) shows 440.72 Hz. (2) For C6 (1046.5 Hz) shows 1042.396606 Hz. Why this happens and how to avoid this problem and detect frequency in more accurate way? Resolution in the frequency domain is inversely related to number of FFT bins. You need to either: increase the size of

This is what I have right now, thanks to Pavel Anossov. I am trying to convert the word frequency that has been outputed into asterisks. import sys import operator from collections import Counter def candidateWord(): with open("sample.txt", 'r') as f: text = f.read() words = [w.strip('!,.?1234567890-=@#$%^&*()_+')for w in text.lower().split()] #word_count[words] = word_count.get(words,0) + 1 counter = Counter(words) print("\n".join("{} {}".format(*p) for p in counter.most_common())) candidateWord() This is what I have right now as an output. how 3 i 2 am 2 are 2 you 2 good 1 hbjkdfd 1 The

I have a large csv file, which is a log of caller data. A short snippet of my file: CompanyName High Priority QualityIssue Customer1 Yes User Customer1 Yes User Customer2 No User Customer3 No Equipment Customer1 No Neither Customer3 No User Customer3 Yes User Customer3 Yes Equipment Customer4 No User I want to sort the entire list by the frequency of occurrence of customers so it will be like: CompanyName High Priority QualityIssue Customer3 No Equipment Customer3 No User Customer3 Yes User Customer3 Yes Equipment Customer1 Yes User Customer1 Yes User Customer1 No Neither Customer2 No User