signal-processing

问题 I'm reading a wave-file and would like to apply the fast fourier transformation to it. However I've got a stereo signal and I'm wondering what to do with the left and right channel. Does the FFT need to be applied to both channels separately? 回答1: Yes and no. Certainly the FFT of each channel is independent, so you want separate FFTs for each of them. However, it is possible to compute two FFTs of real data using one call to a routine for FFTs of complex data and some additional arithmetic.

问题 What kind of solutions are there to analyze beats out of MP3 music in Python? The purpose of this would be to use rhythm information to time the keyframes of generated animation, export animation as video file and and mix the video and audio together. 回答1: Check this: The Echo Nest Remix API # You can manipulate the beats in a song as a native python list beats = audio_file.analysis.beats beats.reverse() 来源： https://stackoverflow.com/questions/6203007/extracting-beats-out-of-mp3-music-with

问题 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? 回答1: Resolution in

问题 Recently I saw a video lecture in my CS 101 class that inspired me to start playing with the WAV File Format in C. My project today has been creating sounds using a simple mathematical sine function. Despite a couple obstacles, my program can now accept several inputs(frequencies of waves, amplitudes of waves, sampling rate, etc.) and create a wav file containing the specified pitches. However, when playing these tones on my computer speakers, there is a strange, rhythmic popping sound, which

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 2 years ago . So I have an array of say 25 samples and I would want to be able to note the trends of whether it's decreasing n or increasing from those 25 sample time interval(basically 25 samples array is my buffer that is being filled by every say 1 ms). Note that it is general trend that I am looking for, not the

问题 I am currently working with some Raman Spectra data, and I am trying to correct my data caused by florescence skewing. Take a look at the graph below: I am pretty close to achieving what I want. As you can see, I am trying to fit a polynomial in all my data whereas I should really just be fitting a polynomial at the local minimas. Ideally I would want to have a polynomial fitting which when subtracted from my original data would result in something like this: Are there any built in libs that

问题 Im really confused over here. I am a ai programmer working on a game that is designed to detect beats in songs and some more. I have no previous knowledge about audio and just reading through whatever material i can find. While i got fft working and stuff I simply don't understand the way samples are transferred to different frequencies. Question 1, what does each frequency stands for. For the algorithm i got. I can transfer for example 1024 samples into 512 outcomes. So are they a

问题 I am working on implementing an algorithm using vDSP. 1) take FFT 2) take log of square of absolute value (can be done with lookup table) 3) take another FFT 4) take absolute value I'm not sure if it is up to me to throw the incoming data through a windowing function before I run the FFT on it. vDSP_fft_zrip(setupReal, &A, stride, log2n, direction); that is my FFT function Do I need to throw the data through vDSP_hamm_window(...) first? 回答1: The iOS Accelerate library function vDSP_fft_zrip()

问题 I have a current signal (extracted in csv) which I obtained from cadence simulation over 30ns time. I have removed DC offset and applied windowing function before FFT. And normalized FFT by sqrt(N) . I have shift zero-frequency component to center of my desired spectrum with fftshift(X) . I got my desired FFT. I also want to get back to my original windowed signal by ifft but it is not showing my windowed signal instead it is showing only a version of the window function that I used. My

问题 I have a current signal (extracted in csv) which I obtained from cadence simulation over 30ns time. I have removed DC offset and applied windowing function before FFT. And normalized FFT by sqrt(N) . I have shift zero-frequency component to center of my desired spectrum with fftshift(X) . I got my desired FFT. I also want to get back to my original windowed signal by ifft but it is not showing my windowed signal instead it is showing only a version of the window function that I used. My