frequency-analysis

I want to develop app to calculate Sound frequency in Android. Android Device will take Sound from microphone (i.e. out side sound) and I have one color background screen in app. on sound frequency changes i have to change background color of screen . So my question is "How can i get sound frequency"? is there any android API available? Please help me out of this problem. ymn Your problem was solved here EDIT: archived here . Also you can analyze the frequency by using FFT . EDIT: FFTBasedSpectrumAnalyzer (example code, the link from the comment) Ashish Wadatkar Thanks for Reply I have done

I am a beginner in R and I have tried to find information about the following without finding anything. The green graph in the picture is composed by the red and yellow graphs. But let's say that I only have the data points of something like the green graph. How do I extract the low/high frequencies (i.e. approximately the red/yellow graphs) using a low pass / high pass filter ? Update: The graph was generated with number_of_cycles = 2 max_y = 40 x = 1:500 a = number_of_cycles * 2*pi/length(x) y = max_y * sin(x*a) noise1 = max_y * 1/10 * sin(x*a*10) plot(x, y, type="l", col="red", ylim=range(

I am trying to count the occurrences of each letter of a word word = input("Enter a word") Alphabet=['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z'] for i in range(0,26): print(word.count(Alphabet[i])) This currently outputs the number of times each letter occurs including the ones that don't. How do I list the letters vertically with the frequency alongside it e.g: word="Hello" H 1 E 1 L 2 O 1 from collections import Counter counts=Counter(word) # Counter({'l': 2, 'H': 1, 'e': 1, 'o': 1}) for i in word: print(i,counts[i]) Try using

Can someone explain how the Count Sketch Algorithm works? I still can't figure out how hashes are used, for example. I have a hard time understanding this paper . This streaming algorithm instantiates the following framework. Find a randomized streaming algorithm whose output (as a random variable) has the desired expectation but usually high variance (i.e., noise). To reduce the variance/noise, run many independent copies in parallel and combine their outputs. Usually 1 is more interesting than 2. This algorithm's 2 actually is somewhat nonstandard, but I'm going to talk about 1 only. Suppose

I have a frequency analysis of words said in episodes of my favorite show. I'm making a plot.barh(s1e1_y, s1e1_x) but it's sorting by words instead of values. The output of >>> s1e1_y is ['know', 'go', 'now', 'here', 'gonna', 'can', 'them', 'think', 'come', 'time', 'got', 'elliot', 'talk', 'out', 'night', 'been', 'then', 'need', 'world', "what's"] and >>>s1e1_x [42, 30, 26, 25, 24, 22, 20, 19, 19, 18, 18, 18, 17, 17, 15, 15, 14, 14, 13, 13] When the plots are actually plotted, the graph's y axis ticks are sorted alphabetically even though the plotting list is unsorted... s1e1_wordlist = []