The array you are showing is the Fourier Transform coefficients of the audio signal. These coefficients can be used to get the frequency content of the audio. The FFT is defined for complex valued input functions, so the coefficients you get out will be imaginary numbers even though your input is all real values. In … Read more
You probably want portable C code here, which your compiler will turn into proper ARM assembly. ARM has conditional moves, and these can be conditional on overflow. The algorithm then becomes: add and conditionally set the destination to unsigned(-1), if overflow was detected. uint16_t add16(uint16_t a, uint16_t b) { uint16_t c = a + b; if … Read more
I want to explain with picture from C3D. In a nutshell, convolutional direction & output shape is important! ↑↑↑↑↑ 1D Convolutions – Basic ↑↑↑↑↑ just 1-direction (time-axis) to calculate conv input = [W], filter = [k], output = [W] ex) input = [1,1,1,1,1], filter = [0.25,0.5,0.25], output = [1,1,1,1,1] output-shape is 1D array example) graph … Read more
You could skip the use of buttord, and instead just pick an order for the filter and see if it meets your filtering criterion. To generate the filter coefficients for a bandpass filter, give butter() the filter order, the cutoff frequencies Wn=[lowcut, highcut], the sampling rate fs (expressed in the same units as the cutoff … Read more
This lets you give frequency, duration, and amplitude, and it is 100% .NET CLR code. No external DLL’s. It works by creating a WAV-formatted MemoryStream which is like creating a file in memory only, without storing it to disk. Then it plays that MemoryStream with System.Media.SoundPlayer. using System; using System.Collections.Generic; using System.IO; using System.Linq; using … Read more
This is challenging to explain in a single StackOverflow post. In general, the simplest beat-detection algorithms work by locating peaks in sound energy, which is easy to detect. More sophisticated methods use comb filters and other statistical/waveform methods. For a detailed explication including code samples, check this GameDev article out.
The complex data is interleaved, with real components at even indices and imaginary components at odd indices, i.e. the real components are at index 2*i, the imaginary components are at index 2*i+1. To get the magnitude of the spectrum at index i, you want: re = fft[2*i]; im = fft[2*i+1]; magnitude[i] = sqrt(re*re+im*im); Then you … Read more
The guy that did AForge did a fairly good job but it’s not commercial quality. It’s great to learn from but you can tell he was learning too so he has some pretty serious mistakes like assuming the size of an image instead of using the correct bits per pixel. I’m not knocking the guy, … Read more
I prefer a Savitzky-Golay filter. It uses least squares to regress a small window of your data onto a polynomial, then uses the polynomial to estimate the point in the center of the window. Finally the window is shifted forward by one data point and the process repeats. This continues until every point has been … Read more
Robust peak detection algorithm (using z-scores) I came up with an algorithm that works very well for these types of datasets. It is based on the principle of dispersion: if a new datapoint is a given x number of standard deviations away from some moving mean, the algorithm signals (also called z-score). The algorithm is … Read more