This answer based on my good understanding of low-pass filters (“exponential moving average” is really just a single-pole lowpass filter), but my hazy understanding of what you’re looking for. I think the following is what you want: First, you can simplify your equation a little bit (looks more complicated but it’s easier in code). I’m … Read more
All the previous answers are correct if you want a technically accurate or scientifically valuable answer. But if you just want a general estimation of comparative loudness, like if you want to check whether the dog is barking or whether a baby is crying and you want to specify the threshold in dB, then it’s … Read more
Here is the sequence of operations to be used for Webrtc_AGC: Create AGC: WebRtcAgc_Create Initialize AGC: WebRtcAgc_Init Set Config: WebRtcAgc_set_config Initialize capture_level = 0 For kAgcModeAdaptiveDigital, invoke VirtualMic: WebRtcAgc_VirtualMic Process Buffer with capture_level: WebRtcAgc_Process Get the out capture level returned from WebRtcAgc_Process and set it to capture_level Repeat 5 to 7 for the audio buffers … Read more
I just got the FFT code working for an iPhone project: create a new project delete all the files except for main.m and xxx_info.plist going to project settings and search for pch and stop it from trying to load a .pch (seeing as we have just deleted it) copy paste the code example over whatever … 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
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 first thing that I would do is see what is already out there. Indeed, this specific problem has already been heavily researched. Here is a brief overview of some really simple methods: link. I must respond to another answer, as well. I do research in signal processing and music information retrieval. On the surface, … Read more
The FFT is fundamentally a change of basis. The basis into which the FFT changes your original signal is a set of sine waves instead. In order for that basis to describe all the possible inputs it needs to be able to represent phase as well as amplitude; the phase is represented using complex numbers. … Read more
A few comments: The Nyquist frequency is half the sampling rate. You are working with regularly sampled data, so you want a digital filter, not an analog filter. This means you should not use analog=True in the call to butter, and you should use scipy.signal.freqz (not freqs) to generate the frequency response. One goal of … 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