i'm working on an application that can help people with disabilites to uses the computer. The application reads values from Arduino+MPU6050 (wich is positioned over the head), and converts it into position. The values from serial ports are like "x,y", and the scale of this values goes from -16000 to +16000.
I processing this values in my computemethod (i report it at bottom of this post). Everything works fine, but i have a problem. If the person that use this software/hardware has some type of muscle spams or tics, the software is too precise and moves the mouse. I want to eliminate this chronical movement...how can i do this?
This is the code of computePosition..
public void computePosition()
{
data = connection.readSeriaLine();
words = data.Split(',');
yaw = words[0];
pitch = words[1];
Int32.TryParse(pitch, out posiY);
Int32.TryParse(yaw, out posiX);
posiX = posiX / headSensitivity;
posiX = posiX - globalPosiX;
posiY = posiY / headSensitivity;
posiY = posiY - globalPosiY;
int signX = Math.Sign(posiX);
int signY = Math.Sign(posiY);
int positionX = Cursor.Position.X;
int positionY = Cursor.Position.Y;
Cursor.Position = new Point(positionX + (signX * movementSensitivity), positionY + (signY * movementSensitivity));
}
Many thanks
I would recommmend you use data processing of movement artefacts in firmware. Sicknesses, like Parkinson desease have tremor at frequencies 1-20 Hz. You can use programmatical filters with adaptation to determined frequency.
To calculate frequency use Fourier transform for accelerometer and gyro raw data.
So, in general you should:
store raw data of sensors in corresonding arrays
do FFT
process filtering for the arrays (cut off dominant frequencies from FFT)
send data to motion processing and then to PC
You can try to do the same on X,Y coordinates in winform app. What way you will use depends on what artefacts you encounter in each particular case. You may even need different filter's setups for warious deseases or self-learning algorithms for each patient.
P.S. You may receive more answers if mark the question with "math" (or similar) tag. Because it lays not in C# or Arduino field.
Update.
Before doing programming i would recommend you to do some research work. First of all you need to gather data from the sensors weared on a man with certain desease. You need at least 30-60 seconds of data containing ticks movement artefacts. Write data in 3 columns:
time in ms | x position | y position
Then go to labchart reader download page, download and install it. Open file with data and do "spectrum" command/view on both X and Y. This will give you info about frequencies which are present (both artefacts and "good" movements). I suppose movement artefact frequencies have bigger amplitude. Remember those frequencies.
Then do "digital filter"-> "band stop" command. And cut off frequencies from previous step. I hope that after this proccessing you will see good charts without artefacts.
Then you can do programming. Implementing FFT and digital filtering is not really hard. For FFT you can use Aforge.Math library.
Related
Trying to make accurate replay system in unity and c#
Hi all,
Im working on a racing game and I decided to add a replay system to allow "ghost car" too, initially I was recordng data in some events like key pressed but only recording that data in all frames I manage a smooth replay, well its still ok as file is not huge and replay works, but the trouble is there is always a slight variation in time, only like 0.1 seconds or 0.2 at the most, I have a list of keyframes and in each position I record a time to be shown, the trouble I think is that because fps vary then not in all runs the same time marks are shown then the winning frame's time is not always being rendered so the winning frame happens in next update slightly after it should be shown. Im using c# and unity just in case, but I think its independent to this mainly. Thanks a lot about any clue, I have been around this issue for some time now
It sounds like you're doing frame-by-frame replay which, as you've discovered, requires your frames to play back with the same delay as the recording. In a game-render loop, that's not guaranteed to happen.
As you record the car states (position, heading, etc) per frame, you need to also record a timestamp (in this case, accumulating Time.deltaTime from race start should suffice).
When you play it back, find the current timestamp and interpolate (ie, Lerp) the car's state from the recorded bounding frames.
Hints for frame interpolation:
class Snapshot {
public float Timestamp;
public Matrix4x4 Transform; // As an example. Put more data here.
}
private int PrevIndex = 0;
private List<Snapshot> Snapshots = (new List<Snapshot>()).OrderBy(m => m.Timestamp).ToList();
private float GetLerpFactor(float currentTimestamp) {
if ( PrevIndex == Snapshots.Count - 1)
return 0; // Reached end of Snapshots
while (currentTimestamp >= Snapshots[PrevIndex + 1].Timestamp)
PrevIndex++; // move 'frame' forward
var currentDelta = Mathf.Max(0f, currentTimestamp - Snapshots[PrevIndex].Timestamp);
var fullDelta = Snapshots[PrevIndex + 1].Timestamp - Snapshots[PrevIndex].Timestamp;
var lerpFactor = currentDelta / fullDelta;
return lerpFactor;
}
Unless for some reason you need to interact with "ghost" car (like collisions) record final data on position/speed/direction at frequent enough original moments of time and interpolate that to new simulation. I would not record raw inputs but rather resulting changes (like gear shifts) unless you need to measure/show how fast user reacted to something.
If you really want to replay the same inputs you'd have to run two separate simulations at the same time so physics and timing of "real" version don't impact "ghost" one, most likely you'll have to again interpolate output of "ghost" simulation to align with real one (unless you have fixed time steps).
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.
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.
I'm sampling a real-world sensor, and I need to display its filtered value. The signal is sampled at a rate of 10 Hz and during that period it could rise as much as 80 per cent of the maximum range.
Earlier I've used Root Mean Square as a filter and just applying it to the last five values I've logged. For this application this wouldn't be good because I don't store unchanged values. In other words, I need to consider time in my filter...
I've read at DSP Guide, but I didn't get much out of it. Is there a tutorial that's pinned specifically at programmers, and not Mathcad engineers? Are there some simple code snippets that could help?
Update: After several spreadsheet tests I've taken the executive decision to log all samples, and apply a Butterworth filter.
You always need to store some values (but not necessarily
all input values). A filter's current output depends on a
number of input values and possibly some past output values.
The simplest filter would be a first order Butterworth low-pass
filter. This would only require you to store one past output
value. The (current) output of the filter, y(n) is:
y(n) = x(n) - a1 * y(n-1)
where x(n) is the current input and y(n-1) is the previous
output of the filter. a1 depends on the cut-off frequency
and the sampling frequency. The cut-off frequency frequency
must be less than 5 Hz (half the sampling frequency),
sufficiently low to filter out the noise, but not so low
that the output will be delayed with respect to the input. And of
course not so low that the real signal is filtered out!
In code (mostly C#):
double a1 = 0.57; //0.57 is just an example value.
double lastY = 0.0;
while (true)
{
double x = <get an input value>;
double y = x - a1 * lastY;
<Use y somehow>
lastY = y;
}
Whether a first order filter is sufficient depends on your
requirements and the characteristics of the input signal (a
higher order filter may be able to suppress more of the
noise at the expense of higher delay of the output signal).
For higher order filters, more values would have to be stored
and the code becomes a little bit more complicated. Usually
the values need to be shifted down in arrays; in an array
for past y values and in an array for past x values.
In DSP, the term "filter" usually refers to the amplification or attenuation (i.e. "lowering") of frequency components within a continuous signal. This is commonly done using Fast Fourier Transform (FFT). FFT starts with a signal recorded over a given length of time (the data are in what's called the "time domain") and transforms these values into what's called the "frequency domain", where the results indicate the strength of the signal in a series of frequency "bins" that range from 0 Hz up to the sampling rate (10 Hz in your case). So, as a rough example, an FFT of one second's worth of your data (10 samples) would tell you the strength of your signal at 0-2 Hz, 2-4 Hz, 4-6 Hz, 6-8 Hz, and 8-10 Hz.
To "filter" these data, you would increase or decrease any or all of these signal strength values, and then perform a reverse FFT to transform these values back into a time-domain signal. So, for example, let's say you wanted to do a lowpass filter on your transformed data, where the cutoff frequency was 6 Hz (in other words, you want to remove any frequency components in your signal above 6 Hz). You would programatically set the 6-8 Hz value to zero and set the 8-10 Hz value to 0, and then do a reverse FFT.
I mention all this because it doesn't sound like "filtering" is really what you want to do here. I think you just want to display the current value of your sensor, but you want to smooth out the results so that it doesn't respond excessively to transient fluctuations in the sensor's measured value. The best way to do this is with a simple running average, possibly with the more recent values weighted more heavily than older values.
A running average is very easy to program (much easier than FFT, trust me) by storing a collection of the most recent measurements. You mention that your app only stores values that are different from the prior value. Assuming you also store the time at which each value is recorded, it should be easy for your running average code to fill in the "missing values" by using the recorded prior values.
I don't have a tutorial that will help you, but in C# you may want to consider using Reactive LINQ - see blog post Reactive programming (II.) - Introducing Reactive LINQ.
As a way to get the events, so you can do your processing without having to store all the values, it would just do the processing as you get the next event in.
To consider time, you could just use an exponential with a negative exponent to decrease the impact of the past measurements.
Yes, for complex real-time systems sampling multiple streams of data, there could be an issue in the data processing (calculation and storage of data) and data consistency.
((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.