signal-processing

I am trying to perform FFT on an audio file to find frequency using the Accelerate framework. I have adapted code (probably wrong) from this question: Spectrogram from AVAudioPCMBuffer using Accelerate framework in Swift Although, the magnitudes from ' spectrum ' are either ' 0 ', ' inf ' or ' nan ', and the ' real ' and ' imag ' components of the complex split print similar results; indicating that this is the cause of the problem as: ' magnitude = sqrt(pow( real ,2)+pow( imag ,2) '. Correct me if i'm wrong, but I think the rest of the code is ok. Why am I receiving these results and how can

I have been reading up on FFT and Pitch Detection for a while now, but I'm having trouble piecing it all together. I have worked out that the Accelerate framework is probably the best way to go with this, and I have read the example code from apple to see how to use it for FFTs. What is the input data for the FFT if I wanted to be running the pitch detection in real time? Do I just pass in the audio stream from the microphone? How would I do this? Also, after I get the FFT output, how can I get the frequency from that? I have been reading everywhere, and can't find any examples or explanations

Anybody know about the fastest method for calculating convolution? Unfortunately the matrix which I deal with is very large (500x500x200) and if I use convn in MATLAB it takes a long time (I have to iterate this calculation in a nested loop). So, I used convolution with FFT and it is faster now. But, I am still looking for a faster method. Any idea? chappjc If your kernel is separable, the greatest speed gains will be realized by performing multiple sequential 1D convolutions. Steve Eddins of MathWorks describes how to take advantage of the associativity of convolution to speed up convolution

Could someone explain how one arrives at the equation below for high pass filtering of the accelerometer values? I don't need mathematical derivation, just an intuitive interpretation of it is enough. #define kFilteringFactor 0.1 UIAccelerationValue rollingX, rollingY, rollingZ; - (void)accelerometer:(UIAccelerometer *)accelerometer didAccelerate:(UIAcceleration *)acceleration { // Subtract the low-pass value from the current value to get a simplified high-pass filter rollingX = (acceleration.x * kFilteringFactor) + (rollingX * (1.0 - kFilteringFactor)); rollingY = (acceleration.y *

I want to implement some image-processing algorithms which are intended to run on a beagleboard . These algorithms use convolutions extensively. I'm trying to find a good C implementation for 2D convolution (probably using the Fast Fourier Transform). I also want the algorithm to be able to run on the beagleboard's DSP, because I've heard that the DSP is optimized for these kinds of operations (with its multiply-accumulate instruction). I have no background in the field so I think it won't be a good idea to implement the convolution myself (I probably won't do it as good as someone who

I saw many examples about filtering Accelermeter's values, to cancel out the gravity (High-pass filter). But most of them were 1st order one, which is said to be simple, but laggy and not the best one (though I know nothing about the filters or DSP). Somebody in here said that there exists the better solution used in DSP to sense the motion. Regretfully I can't even imagine what it would be, as I am completely outsider to the field. I hope somebody could tell at least the type of filters that can be used when dealing with the sensor. Of course, even brief introduction to the specific algorithm