帮助FFT（快速傅立叶变换）和/或DSP

Question

Im trying to do a screen-flashing application, that flashes the screen according to the music(which will be frequencies, such as healing frequencies, etc...). I already made the player and know how will I make the screen flash, but I need to make the screen flash super fast according to the music, for example if the music speeds up, the screen-flash will flash faster. I understand that I would achieve this by FFT or DSP(as I only need to know when the frequency raises from some Hz, lets say 20 to change the color, making the screen-flash).

But I've found that I understand NOTHING, even less try to implement it to my application.

Can somebody help me out to learn whichever both of them? My email is sismetic_chaos@hotmail.com. I really need help, I've been stucked for like 3 days not coding or doing anything at all, trying to understand, but I dont.

PS:My application is written in C++ and Qt.

PS:Thanks for taking the time to read this and the willingness to help.

Edit: Thanks to all for the answers, the problem is in no way solved yet, but I appreciate all the answers, I didnt thought I would get so many answers and info. Thanks to you all.

Answer 1

The output of a FFT will give you the frequency spectrum of an audio sample, but extracting the tempo from the FFT output is probably not the way you want to go.

One thing you can do is to use peak detection to identify the volume "spikes" that typically occur on the "down-beats" of the music. If you can identify the down-beats, then you can use a resource like bpmdatabase.com to find the tempo of the song. The tempo will tell you how fast to flash and the peaks you detected will tell you when to start flashing. Have your app monitor your flashes to make sure that they generally occur at the same time as a peak (if the two start to diverge, then the tempo may have changed mid-song).

That may sound straightforward, but this is actually a very non-trivial thing to implement. You might want to read this SO question for more information. There are some quality links in the answers there.

If I'm completely mis-interpreting what you are trying to do and you need to do FFTs for something different, then you might want to look at using one of the existing FFT libraries to do the heavy lifting for you. Some examples are FFTW and KissFFT .

Answer 2

This is a difficult problem, requiring more than an FFT. I'll briefly describe how I implemented beat detection when I was writing software for professional DJ equipment.

First of all, you'll need to cut down the amount of data you're dealing with, since there are only two or three beats per second, but tens of thousands of samples. You'll also need to look at different frequency ranges, since some types of music carry the tempo in the bassline, and others in percussion or other instruments. So pass the signal through several band-pass filters (I chose 8 filters, each covering one octave, from low bass to high treble), and then downsample each band by averaging the power over a few hundred samples.

Every few seconds, you'll have a thousand or so samples in each band. Your next tool is an autocorrelation, to identify repetitive patterns in the music. The peaks of the autocorrelation tell you what the beat is more or less likely to be; but you'll need to make up some heuristics to compare all the frequency bands to find a beat that you can be confident in, and to avoid misleading syncopations. If you can manage that, then you'll have a reasonable guess at the tempo, but no idea of the phase (ie exactly when to flash the screen).

Now you can look at the a smoothed version of the audio data for peaks, some of which are likely to correspond to beats. Initially, look for the strongest peak over the course of a few seconds and take that as a downbeat. In conjunction with the tempo you estimated in the first stage, you can predict when the next beat is due, and measure where you actually saw something like a beat, and adjust your estimate to more closely match the data. You can also maintain a confidence level based on how well the predicted beats match the measured peaks; if that drops too low, then restart the beat detection from scratch.

There are a lot of fiddly details to this, and it took me some weeks to get it working nicely. It is a difficult problem.

Or for a simple visualisation effect, you could simply detect peaks and flash the screen for each one; it will probably look good enough.

Answer 3

It sounds like maybe you're trying to get your visualizer to flash the screen in time with the music somehow. I don't think calculating the FFT is going to help you here. At any given instant, there will be many simultaneous frequency components, all over the audio spectrum (roughly 20 Hz to 20 kHz). But you're likely to be a lot more interested in the musical tempo (beats per minute -- more like 5 Hz or below), and that's not going to show up anywhere in an FFT of the raw audio signal.

You probably need something much simpler -- some sort of real-time peak detection. Whenever you see a peak greater than some threshold above the average volume, make your screen flash.

Of course, more complicated visualizations might well take advantage of the FFT, but not the one you're describing.

Answer 4

My recommendation would be to find a library that does this for you. Unless you have a lot of mathematics to back you up, I think you will be wasting a ton of your time trying to learn FFTs when all you really want out is some sort of 'base hits per minute' number out which you can adjust your graphics to accordingly.

Check out this similar post: here

It took me about three weeks to understand the mathematics behind FFTs and then another week to write something in Matlab using those concepts. If you are discouraged after three days, don't try and roll your own.

I hope this is helpful advice and not discouraging.

-Brian J. Stinar-

Answer 5

As previous answers have noted, an FFT is probably not the tool you need in order to solve your problem, which requires tempo detection rather than spectral analysis.

For an example of what can be done using FFT - and of how a particular FFT implementation was integrated into a Qt application, take a look at this blog post which describes the spectrum analyzer demo I developed. Code for the demo is shipped with Qt itself, in the demos/spectrum directory.