I have a current implementation of Gaussian Blur using regular convolution. It is efficient enough for small kernels, but once the kernels size gets a little bigger, the performance takes a hit. So, I
I have 5 recorded wav files. I want to compare the new incoming recordings with these files and determine which one it resembles most.
I am working on a music visualizer and I\'d like to display a different visual element for each instrument.For example, blue bar representing vocal, red bar representing guitar, yellow bar representin
I am transforming an image to a frequency spectrum, convolving it with a kernel, then inverse-transforming it back.
I\'m pretty new to Image Processing and found out that the FFT convolution speeds up the convolution with large kernel sizes a lot.
Can anyone please explain how to perform template matching using FFT. The template is smaller than the original image.
What processor will perform better, i5-2500K or i7-960, regarding certain FFT operations per second, for example: complex FFT in-place on 16k buffer length?
I am trying to implement a channel vocoder using the iOS Accelerate vDSP FFT algorithms. I am having trouble figuring out how to treat the DC component and Nyquist frequency.
I have an assignment to implement a Ram-Lak filter, but nearly no information given on it (except look at fft, ifft, fftshift, ifftshift).
I need some help understanding the output of the DFT/FFT computation. I\'m an experienced software engineer and need to interpret some smartphone accelerometer readings, such as finding the principal
加载中,请稍侯......