Hi I'm a noob in audio related coding and I'm working in a pitch tracking DLL that I will use to try to create a sort of open-source version of the video-game Rocksmith as a learning experience.
So far I have managed to get the FFT to work so I can detect pitch frequency (Hz) then by using an algorithm and the table below I can manage to determine the octave (2th to 6th) and the note (C to B) for played note.
The next step is to detect the string so I can determine the fret.
I've been thinking about it and in theory I can work with this, I will know when the user is playing the right note but the game could be "Hack" because by just using the Hz the game is not able to detect if a note is played in the right string. For example 5th string + 1th fret = C4 261.63Hz is equals to 6th string + 5th fret = C4 261.63Hz.
The chances of having an user playing a note in the wrong string and getting it right is low, but I think it would be really good to know the string so I can provide to the users some error feedback when they play the wrong string (Like you should go a string up or down).
Do you know what can I do to detect the string? Thanks in advance :)
[edit]
The guitar and strings that we are using affect the timbre so analyzing the timbre seems to not be a easy way of detecting strings:
"Variations in timbre on your guitar are produced by an enormous number of factors from pickup design and position, the natural resonances and damping in your guitar due to the wood used (that's a different sort of timber!) and its construction and shape, the gauge and age of your strings, your playing technique, where you fret and pluck the string, and so on."
This might be a little bit late because the post is one years old. But here's a solution, which I found out after long research for pitch detecting a guitar.
THIS IS WHY FFT DOESN'T WORK :
You cannot use FFT since the result gives you a linear array, and the sound is calculated logarithmically (exponential distance between notes). Plus, FFT gives you an array of bins in which your frequency COULD BE, it doesnt give you the precise result.
THIS IS WHAT I SUGGEST :
Use dywapitchtrack. it's a library that uses a wavelet algorythm, which works directly on your wave instead of calculating large bins like FFT.
description:
The dywapitchtrack is based on a custom-tailored algorithm which is of very high quality:
both very accurate (precision < 0.05 semitones), very low latency (< 23 ms) and
very low error rate. It has been thoroughly tested on human voice.
It can best be described as a dynamic wavelet algorithm (dywa):
DOWNLOAD : https://github.com/inniyah/sndpeek/tree/master/src/dywapitchtrack
USE(C++):
put the .c and .h where you need it and import it in your project
include the header file
//Create a dywapitchtracker Object
dywapitchtracker pitchtracker;
//Initialise the object with this function
dywapitch_inittracking(&pitchtracker);
When your buffer is full (buffer needs to be at 44100 resolution and power of 2 of length, mine is 2048):
//use this function with your buffer
double thePitch = dywapitch_computepitch(&pitchtracker, yourBuffer, 0, 2048);
And voilà, thePitch contains precisely what you need. (feel free to ask question if something is unclear)
An simple FFT peak estimator is not a good guitar pitch detector/estimator, due to many potentially strong overtones. There exist more robust pitch estimation algorithms (search stackoverflow and DSP.stackexchange). But if you require the players to pre-characterize each string on their individual instruments, both open and fretted, before starting the game, an FFT fingerprint of those characterizations might be able to differentiate the same note played on different strings on some guitars. The thicker strings will give off slightly different ratios of energy in some of the higher overtones, as well as different amounts of slight inharmonicity.
The other answers seem to suggest a simple pitch detection method. However, it is something you will have to research.
Specifically, compare the overtones of 5th string 1st fret to sixth string 5th fret. that is, only look at 261.63*2, 261.63*3, *4, etc. Also, try looking at 261.63*0.5. Compare the amplitudes of the two signals at these freqs. There might be a pattern that could be detected.
Related
I am trying to implement a volume meter to help users select their microphone using NAudio. I need to do my best to weed out devices that just have background noise and insure I show something when they talk.
We are currently using version 1.7.3 within a Unity3D application so none of the MMDevice related approaches are available as they crash.
I am using a WaveInEvent that I feed into a WaveInProvider that I subsequently feed to a SampleChannel. I feed the SampleChannel into a MeteringSampleProvider which I have subscribed to the StreamVolume event.
In my OnPostVolumeMeter event handler when I receive the StreamVolumeEventArgs (I named the parameter e) I'm wondering how to calculate decibels. I have seen plenty of examples that fish out the peak volume (or sometimes it seems to be referred to as an amplitude) from e.MaxSampleValues[0]. Some examples check whether it is a stereo signal and will grab the max between e.MaxSampleValues[0] or e.MaxSampleValues[1].
Anyway, what are the values of this number? Is it a percentage? They are relatively small decimals (10^-3 or 10^-4) when I hillbilly debug to the console.
Is the calculation something like,
var peak = e.MaxSampleValues[0];
if (e.MaxSampleValues.Length > 1)
{
peak = Mathf.Max(e.MaxSampleValues[0], e.MaxSampleValues[1]);
}
var dB = Mathf.Max(20.0f*Mathf.Log10(peak), -96.0f);
or do I need to divide peak by 32768.0? As in,
var dB = Mathf.Max(20.0f*Mathf.Log10(peak/32768.0f), -96.0f);
Is this approach totally incorrect and I need to collect a buffer of samples that I do an RMS sort of calculation where I calculate the square root of the sum of the averages divided by the number of samples all divided by 32768 and feed that into the Log10?
I've seen several references to look at the AudioPlaybackPanel of the NAudioDemo and it sets the volumeMeter Amplitude to be the values of e.MaxSampleValues[0] and e.MaxSampleValues[1]
looking at the date of your post this is probably a solved issue for you but of the benefit of others here goes.
Audio signals swing between negative and positive values in a wave. The frequency of the swing and the Amplitude or height of the swing effect what you hear.
You are correct in saying you are looking for the amplitude to see if audio is present.
For a meter as the sample rate is much higher than the refresh rate of any meter you are likely to display, you will need to either record the peak using math.max or do an average over a number of samples. In your case either would work, unless you are trying to show an accurate meter in bdFS the db calculation would not be needed.
In apps where I have been looking to trigger things based on the presence of audio or lack their of. I normally convert the samples to a float this will give you a range between 0 and 1 and then pick a threshold say 0.2 and say if any sample is above that we have audio.
a float also provides a nice indicative meter for display. Note if your app was for a pro audio application and you were asking about accurate metering my answer would be totally different.
Hello
I'm exploring the audio possibilities of the WP7 platform and the first stumble I've had is trying to implement a FFT using the Cooley-Tukey method. The result of that is that the spectrogram shows 4 identical images in this order: one normal, one reversed, one normal, one reversed.
The code was taken from another C# project (for desktop), the implementation and all variables seem in place with the algorithm.
So I can see two problems right away: reduced resolution and CPU wasted to generate four identical spectrograms.
Given a sample size of 1600 (could be 2048) I know have only 512 usable frequency information which leaves me with a 15Hz resolution for an 8kHz frequency span. Not bad, but not so good either.
Should I just give up on the code and use NAudio? I cannot seem to have an explanation why the spectrum is quadrupled, input data is ok, algorithm seems ok.
This sounds correct. You have 2 mirrors, I can only assume that one is the Real part and the other is the Image part. This is standard FFT.
From the real and image you can compute the magnitude or amplitude of each harmonic which is more common or compute the angle or phase shift of each harmonic which is less common.
Gilad.
I switched to NAudio and now the FFT works. However I might have found the cause (I probably won't try to test again): when I was constructing an array of double to feed into the FFT function, I did something like:
for (int i = 0; i < buffer.Length; i+= sizeof(short))
{
samples[i] = ReadSample(buffer, i);
}
For reference, 'samples' is the double[] input to fft, ReadSample is something that takes care of little/big endian. Can't remember right now how the code was, but it was skipping every odd sample.
My math knowledge has never been great but I'm thinking this induces some aliasing patterns which might in the end produce the effect I experienced.
Anyway, problem worked around, but thanks for your input and if you can still explain the phenomenon I am grateful.
I want to do smoothing to an image in the frequency domain. when i use google to see any articles it gave some Matlab codes which i don't need. i could do FFT to an image but i don't know how to implement any smoothing techniques(ILPF, BLPF, IHPF, BHPF) in frequency domain. if you can provide any code samples for any of the above techniques WITHOUT using any image processing libraries it will be really helpful and C# is preferred.
Thanks,
Could you define what you mean by 'smoothing in the frequency domain'? You can generate a spectrum image using FFT and multiply the image by some function to attenuate particular frequencies, then convert the spectrum back to an image using the inverse-FFT. However, for this kind of filtering (multiplication by some scaling function in frequency), you can achieve the same result more quickly by convolving with the dual function in the spatial domain.
In any case, if you wish to implement this yourself, read up on FFT (the fast Fourier transform) and convolution. You might also check out a signal processing textbook, if you're interested, as the theory behind discrete filtering is fairly deep. The algorithms won't make a whole lot of sense without that theory, though you can certainly apply them without understanding them.
If you want to implement your own DSP algorithms, check out this book online. In particular, Ch 33 describes the math and algorithm behind Butterworth filter design. Ch 12 describes how to implement FFT.
There is a great series on Code Project by Christian Graus which you might find useful, especially part 2 which deals amongst others with smoothing filters:
Image Processing for Dummies with C# and GDI+ Part 1 - Per Pixel Filters
Image Processing for Dummies with C# and GDI+ Part 2 - Convolution Filters
Image Processing for Dummies with C# and GDI+ Part 3 - Edge Detection Filters
Image Processing for Dummies with C# and GDI+ Part 4 - Bilinear Filters and Resizing
Image Processing for Dummies with C# and GDI+ Part 5 - Displacement filters, including swirl
Image Processing for Dummies with C# and GDI+ Part 6 - The HSL color space
Keshan, it is simple. Imagine the FFT is another two pictures where low frequencies lie in the middle and high frequencies away from the middle. If the pixels are numbered from -w/2 to w/2 and -h/2 to h/2 you can simply measure the distance from the middle as a(x,y)=sqrt(x^2+y^2). Then take some arbitrary monotonic decreasing function like f(x)=1/(1+x) and multiply each point in the fft with f(a(x,y)). Then transform back using the FFT.
There are different choices for f(x) which will look different. For example a gaussian function or bessel or whatever. I did this for my undergrad and it was great fun. If you send me a mail I will send you my program :-).
One bit caveat is the ordering in output of the fft. The arrays it generates can be ordered in weird ways. It is important that you find out which array index corresponds to which x/y-position in the "analytical" fourier transform!
For all image/signal processing I recommend OpenCV.
This has a managed C# wrapper: Emgu.
http://www.emgu.com/wiki/index.php/Main_Page
((Answer selected - see Edit 5 below.))
I need to write a simple pink-noise generator in C#. The problem is, I've never done any audio work before, so I don't know how to interact with the sound card, etc. I do know that I want to stay away from using DirectX, mostly because I don't want to download a massive SDK just for this tiny project.
So I have two problems:
How do I generate Pink Noise?
How do I stream it to the sound card?
Edit: I really want to make a pink noise generator... I'm aware there are other ways to solve the root problem. =)
Edit 2: Our firewall blocks streaming audio and video - otherwise I'd just go to www.simplynoise.com as suggested in the comments. :(
Edit 3: I've got the generation of white-noise down, as well as sending output to the sound card - now all I need to know is how to turn the white-noise into pink noise. Oh - and I don't want to loop a wav file because every application I've tried to use for looping ends up with a tiny little break in between loops, which is jarring enough to have prompted me in this direction in the first place...
Edit 4: ... I'm surprised so many people have jumped in to very explicitly not answer a question. I probably would have gotten a better response if I lied about why I need pink noise... This question is more about how to generate and stream data to the sound card than it is about what sort of headphones I should be using. To that end I've edited out the background details - you can read about it in the edits...
Edit 5: I've selected Paul's answer below because the link he provided gave me the formula to convert white noise (which is easily generated via the random number generator) into pink noise. In addition to this, I used Ianier Munoz's CodeProject entry "Programming Audio Effects in C#" to learn how to generate, modify, and output sound data to the sound card. Thank you guys for your help. =)
Maybe you can convert the C/C++ code here to C#:
http://www.firstpr.com.au/dsp/pink-noise/
The easiest way to get sound to the sound card is to generate a wav (spit out some hardcoded headers and then sample data). Then you can play the .wav file.
Pink noise is just white noise put through a -3dB/octave LPF. You can generate white noise using rand() (or any function that generates uniformly random numbers).
Streaming stuff to the soundcard is reasonably trivial, as long as you have Google handy. If you choose to avoid DirectX, consider using PortAudio or ASIO for interfacing with the soundcard... although I think you're gonna have to use C++ or C.
Other than that, why waste CPU time generating it? Loop a damn WAV file!
bit late to this i realise, but anyone coming across it for answers should know that pink noise is white noise with -3dB/octave, not -6 as stated above, which is actually brown noise.
Here's a very simple way to create pink noise, which just sums lots of waves spaced logarithmically apart, together! It may be too slow for your purposes if you want the sound created in realtime, but further optimization is surely possible (e.g: a faster cosine function).
The functions outputs a double array with values from -1 to 1. This represents the lowest and highest points in the waveform respectively.
The quality parameter represents the number of waves produced to make the sound. I find 5000 waves (about 40 intervals per semitone) is just about the threshold where I can't detect any noticeable improvement with higher values, but to be on the safe side, you could (optionally) increase this to about 10,000 waves or higher. Also, according to Wikipedia, 20 hertz is around the lower limit of human perception in terms of what we can hear, but you can change this too if you want.
Note the sound gets quieter with a higher quality value due to technical reasons, so you may (optionally) want to adjust the volume via the volumeAdjust parameter.
public double[] createPinkNoise(double seconds, int quality=5000, double lowestFrequency=20, double highestFrequency = 20000, double volumeAdjust=1.0)
{
long samples = (long)(44100 * seconds);
double[] d = new double[samples];
double[] offsets = new double[samples];
double lowestWavelength = highestFrequency / lowestFrequency;
Random r = new Random();
for (int j = 0; j < quality; j++)
{
double wavelength = Math.Pow(lowestWavelength, (j * 1.0) / quality) * 44100 / highestFrequency;
double offset = r.NextDouble() * Math.PI*2; // Important offset is needed, as otherwise all the waves will be almost in phase, and this will ruin the effect!
for (long i = 0; i < samples; i++)
{
d[i] += Math.Cos(i * Math.PI * 2 / wavelength + offset) / quality * volumeAdjust;
}
}
return d;
}
Here's is an example of what the playback thread looks like. I'm using DirectSound to create a SecondaryBuffer where the samples are written. As you can see it's pretty straightforward:
/// <summary>
/// Thread in charge of feeding the playback buffer.
/// </summary>
private void playbackThreadFn()
{
// Begin playing the sound buffer.
m_playbackBuffer.Play( 0, BufferPlayFlags.Looping );
// Change playing state.
IsPlaying = true;
// Playback loop.
while( IsPlaying )
{
// Suspend thread until the playback cursor steps into a trap...
m_trapEvent.WaitOne();
// ...read audio from the input stream... (In this case from your pink noise buffer)
Input.Collect( m_target, m_target.Length );
// ...calculate the next writing position...
var writePosition = m_traps[ ((1 & m_pullCounter++) != 0) ? 0 : 1 ].Offset;
// ...and copy audio to the device buffer.
m_playbackBuffer.Write( writePosition, m_deviceBuffer, LockFlag.None );
}
// Stop playback.
m_playbackBuffer.Stop();
}
If you need more details on how it works I'll be glad to help.
If you're on Linux, you can use SOX (you may have it already, try the play command).
play -t sl - synth 3 pinknoise band -n 1200 200 tremolo .1 40 < /dev/zero
As a quick and dirty way to do it, how about just looping a pink noise wav in your audio player? (Yes, I know part of the fun is to make it yourself....)
What about an .mp3 sample of Pink Noise on repeat?
You could use Audacity to generate as much pink noise as you want, and then repeat it.
Or you could dig into the source code and see how Audacity does the pink noise generation.
I can't speak about C#, but you might be better off with some good noise canceling headphones and your favorite mp3's.
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
I'd like to write a simple C# application to monitor the line-in audio and give me the current (well, the rolling average) beats per minute.
I've seen this gamedev article, and that was absolutely no help. I went through and tried to implement what he was doing but it just wasn't working.
I know there have to be tons of solutions for this, because lots of DJ software does it, but I'm not having any luck in finding any open-source library or instructions on doing it myself.
Calculate a powerspectrum with a sliding window FFT:
Take 1024 samples:
double[] signal = stream.Take(1024);
Feed it to an FFT algorithm:
double[] real = new double[signal.Length];
double[] imag = new double[signal.Length);
FFT(signal, out real, out imag);
You will get a real part and an imaginary part. Do NOT throw away the imaginary part. Do the same to the real part as the imaginary. While it is true that the imaginary part is pi / 2 out of phase with the real, it still contains 50% of the spectrum information.
EDIT:
Calculate the power as opposed to the amplitude so that you have a high number when it is loud and close to zero when it is quiet:
for (i=0; i < real.Length; i++) real[i] = real[i] * real[i];
Similarly for the imaginary part.
for (i=0; i < imag.Length; i++) imag[i] = imag[i] * imag[i];
Now you have a power spectrum for the last 1024 samples. Where the first part of the spectrum is the low frequencies and the last part of the spectrum is the high
frequencies.
If you want to find BPM in popular music you should probably focus on the bass. You can pick up the bass intensity by summing the lower part of the power spectrum. Which numbers to use depends on the sampling frequency:
double bassIntensity = 0;
for (i=8; i < 96; i++) bassIntensity += real[i];
Now do the same again but move the window 256 samples before you calculate a new spectrum. Now you end up with calculating the bassIntensity for every 256 samples.
This is a good input for your BPM analysis. When the bass is quiet you do not have a beat and when it is loud you have a beat.
Good luck!
There's an excellent project called Dancing Monkeys, which procedurally generates DDR dance steps from music. A large part of what it does is based on (necessarily very accurate) beat analysis, and their project paper goes into much detail describing the various beat detection algorithms and their suitability to the task. They include references to the original papers for each of the algorithms. They've also published the matlab code for their solution. I'm sure that between those you can find what you need.
It's all available here: http://monket.net/dancing-monkeys-v2/Main_Page
Not that I have a clue how to implement this, but from an audio engineering perspective you'd need to filter first. Bass drum hits would be the first to check. A low pass filter that gives you anything under about 200Hz should give you a pretty clear picture of the bass drum. A gate might also be necessary to cleanup any clutter from other instruments with harmonics that low.
The next to check would be snare hits. You'd have to EQ this one. The "crack" from a snare is around 1.5kHz from memory, but you'd need to definitely gate this one.
The next challenge would be to work out an algorithm for funky beats. How would you programatically find beat 1? I guess you'd keep track of previous beats and use a pattern matching something-or-other. So, you'd probably need a few bars to accurately find the beat. Then there's timing issues like 4/4, 3/4, 6/8, wow, I can't imagine what would be required to do this accurately! I'm sure it'd be worth some serious money to audio hardware/software companies.
This is by no means an easy problem. I'll try to give you an overview only.
What you could do is something like the following:
Compute the average (root-mean-square) loudness of the signal over blocks of, say, 5 milliseconds. (Having never done this before, I don't know what a good block size would be.)
Take the Fourier transform of the "blocked" signal, using the FFT algorithm.
Find the component in the transformed signal that has the largest magnitude.
A Fourier transform is basically a way of computing the strength of all frequencies present in the signal. If you do that over the "blocked" signal, the frequency of the beat will hopefully be the strongest one.
Maybe you need to apply a filter first, to focus on specific frequencies (like the bass) that usually contain the most information about the BPM.
I found this library which seem to have a pretty solid implementation for detecting Beats per Minute.
https://github.com/owoudenberg/soundtouch.net
It's based on http://www.surina.net/soundtouch/index.html which is used in quite a few DJ projects http://www.surina.net/soundtouch/applications.html
First of all, what Hallgrim is producing is not the power spectral density function. Statistical periodicities in any signal can be brought out through an autocorrelation function. The fourier transform of the autocorrelation signal is the power spectral density. Dominant peaks in the PSD other than at 0 Hz will correspond to the effective periodicity in the signal (in Hz)...
The easy way to do it is to have the user tap a button in rhythm with the beat, and count the number of taps divided by the time.
I'd recommend checking out the BASS audio library and the BASS.NET wrapper. It has a built in BPMCounter class.
Details for this specific function can be found at
http://bass.radio42.com/help/html/0833aa5a-3be9-037c-66f2-9adfd42a8512.htm.