audioRecord = new AudioRecord(MediaRecorder.AudioSource.MIC, 8000,
AudioFormat.CHANNEL_IN_STEREO, AudioFormat.ENCODING_PCM_16BIT,
bufferSizeInBytes);
readsize = audioRecord.read(audiodata, 0, bufferSizeInBytes);
So,
Q1. how is the 16bit PCM encoded sampling point is saved in the andiodata which is consist of bytes(8bit)? someone says every two bytes saves one sampling point while someone just ignores it.
Q2. if so which is the high 8bit? the audiodata[2*i] or audiodata[2*i+1]?
Q3. shall we consider the sign issue when we return to 16bit sampling point by two bytes?
finally, i got the answer:instead of saving in byte (which seems to be a common misunderstanding and all programmer are doing like this), i save the sampling data in a short type buffer, this avoids the three problems mentioned above. i have approved the sampling effectiveness by signal with different frequency generated from matlab. the code is as follow:
`short[] audiodata = new short[bufferSizeInBytes/2];
readsize = audioRecord.read(audiodata, 0, bufferSizeInBytes/2);
`
this is the real sampling value for FFT and digital communication from earphone interface.
Related
How can I draw an spectrum for an given audio file with Bass library?
I mean the chart similar to what Audacity generates:
I know that I can get the FFT data for given time t (when I play the audio) with:
float fft[1024];
BASS_ChannelGetData(chan, fft, BASS_DATA_FFT2048); // get the FFT data
That way I get 1024 values in array for each time t. Am I right that the values in that array are signal amplitudes (dB)? If so, how the frequency (Hz) is associated with those values? By the index?
I am an programmer, but I am not experienced with audio processing at all. So I don't know what to do, with the data I have, to plot the needed spectrum.
I am working with C++ version, but examples in other languages are just fine (I can convert them).
From the documentation, that flag will cause the FFT magnitude to be computed, and from the sounds of it, it is the linear magnitude.
dB = 10 * log10(intensity);
dB = 20 * log10(pressure);
(I'm not sure whether audio file samples are a measurement of intensity or pressure. What's a microphone output linearly related to?)
Also, it indicates the length of the input and the length of the FFT match, but half the FFT (corresponding to negative frequencies) is discarded. Therefore the highest FFT frequency will be one-half the sampling frequency. This occurs at N/2. The docs actually say
For example, with a 2048 sample FFT, there will be 1024 floating-point values returned. If the BASS_DATA_FIXED flag is used, then the FFT values will be in 8.24 fixed-point form rather than floating-point. Each value, or "bin", ranges from 0 to 1 (can actually go higher if the sample data is floating-point and not clipped). The 1st bin contains the DC component, the 2nd contains the amplitude at 1/2048 of the channel's sample rate, followed by the amplitude at 2/2048, 3/2048, etc.
That seems pretty clear.
How might one generate audio at runtime using C++? I'm just looking for a starting point. Someone on a forum suggested I try to make a program play a square wave of a given frequency and amplitude.
I've heard that modern computers encode audio using PCM samples: At a give rate for a specific unit of time (eg. 48 kHz), the amplitude of a sound is recorded at a given resolution (eg. 16-bits). If I generate such a sample, how do I get my speakers to play it? I'm currently using windows. I'd prefer to avoid any additional libraries if at all possible but I'd settle for a very light one.
Here is my attempt to generate a square wave sample using this principal:
signed short* Generate_Square_Wave(
signed short a_amplitude ,
signed short a_frequency ,
signed short a_sample_rate )
{
signed short* sample = new signed short[a_sample_rate];
for( signed short c = 0; c == a_sample_rate; c++ )
{
if( c % a_frequency < a_frequency / 2 )
sample[c] = a_amplitude;
else
sample[c] = -a_amplitude;
}
return sample;
}
Am I doing this correctly? If so, what do I do with the generated sample to get my speakers to play it?
Your loop has to use c < a_sample_rate to avoid a buffer overrun.
To output the sound you call waveOutOpen and other waveOut... functions. They are all listed here:
http://msdn.microsoft.com/en-us/library/windows/desktop/dd743834(v=vs.85).aspx
The code you are using generates a wave that is truly square, binary kind of square, in short the type of waveform that does not exist in real life. In reality most (pretty sure all) of the sounds you hear are a combination of sine waves at different frequencies.
Because your samples are created the way they are they will produce aliasing, where a higher frequency masquerades as a lower frequency causing audio artefacts. To demonstrate this to yourself write a little program which sweeps the frequency of your code from 20-20,000hz. You will hear that the sound does not go up smoothly as it raises in frequency. You will hear artefacts.
Wikipedia has an excellent article on square waves: https://en.m.wikipedia.org/wiki/Square_wave
One way to generate a square wave is to perform an inverse Fast Fourier Transform which transforms a series of frequency measurements into a series of time based samples. Then generating a square wave is a matter of supplying the routine with a collection of the measurements of sin waves at different frequencies that make up a square wave and the output is a buffer with a single cycle of the waveform.
To generate audio waves is computationally expensive so what is often done is to generate arrays of audio samples and play them back at varying speeds to play different frequencies. This is called wave table synthesis.
Have a look at the following link:
https://www.earlevel.com/main/2012/05/04/a-wavetable-oscillator%E2%80%94part-1/
And some more about band limiting a signal and why it’s necessary:
https://dsp.stackexchange.com/questions/22652/why-band-limit-a-signal
I'm trying to work with this camera SDK, and let's say the camera has this function called CameraGetImageData(BYTE* data), which I assume takes in a byte array, modifies it with the image data, and then returns a status code based on success/failure. The SDK provides no documentation whatsoever (not even code comments) so I'm just guestimating here. Here's a code snippet on what I think works
BYTE* data = new BYTE[10000000]; // an array of an arbitrary large size, I'm not
// sure what the exact size needs to be so I
// made it large
CameraGetImageData(data);
// Do stuff here to process/output image data
I've run the code w/ breakpoints in Visual Studio and can confirm that the CameraGetImageData function does indeed modify the array. Now my question is, is there a standard way for cameras to output data? How should I start using this data and what does each byte represent? The camera captures in 8-bit color.
Take pictures of pure red, pure green and pure blue. See what comes out.
Also, I'd make the array 100 million, not 10 million if you've got the memory, at least initially. A 10 megapixel camera using 24 bits per pixel is going to use 30 million bytes, bigger than your array. If it does something crazy like store 16 bits per colour it could take up to 60 million or 80 million bytes.
You could fill this big array with data before passing it. For example fill it with '01234567' repeated. Then it's really obvious what bytes have been written and what bytes haven't, so you can work out the real size of what's returned.
I don't think there is a standard but you can try to identify which values are what by putting some solid color images in front of the camera. So all pixels would be approximately the same color. Having an idea of what color should be stored in each pixel you may understand how the color is represented in your array. I would go with black, white, reg, green, blue images.
But also consider finding a better SDK which has the documentation, because making just a big array is really bad design
You should check the documentation on your camera SDK, since there's no "standard" or "common" way for data output. It can be raw data, it can be RGB data, it can even be already compressed. If the camera vendor doesn't provide any information, you could try to find some libraries that handle most common formats, and try to pass the data you have to see what happens.
Without even knowing the type of the camera, this question is nearly impossible to answer.
If it is a scientific camera, chances are good that it adhers to the IEEE 1394 (aka IIDC or DCAM) standard. I have personally worked with such a camera made by Hamamatsu using this library to interface with the camera.
In my case the camera output was just raw data. The camera itself was monochrome and each pixel had a depth-resolution of 12 bit. Therefore, each pixel intensity was stored as 16-bit unsigned value in the result array. The size of the array was simply width * height * 2 bytes, where width and height are the image dimensions in pixels the factor 2 is for 16-bit per pixel. The width and height were known a-priori from the chosen camera mode.
If you have the dimensions of the result image, try to dump your byte array into a file and load the result either in Python or Matlab and just try to visualize the content. Another possibility is to load this raw file with an image editor such as ImageJ and hope to get anything out from it.
Good luck!
I hope this question's solution will helps you: https://stackoverflow.com/a/3340944/291372
Actually you've got an array of pixels (assume 1 byte per pixel if you camera captires in 8-bit). What you need - is just determine width and height. after that you can try to restore bitmap image from you byte array.
I've run into some nasty problem with my recorder. Some people are still using it with analog tuners, and analog tuners have a tendency to spit out 'snow' if there is no signal present.
The Problem is that when noise is fed into the encoder, it goes completely crazy and first consumes all CPU then ultimately freezes. Since main point od the recorder is to stay up and running no matter what, I have to figure out how to proceed with this, so encoder won't be exposed to the data it can't handle.
So, idea is to create 'entropy detector' - a simple and small routine that will go through the frame buffer data and calculate entropy index i.e. how the data in the picture is actually random.
Result from the routine would be a number, that will be 0 for completely back picture, and 1 for completely random picture - snow, that is.
Routine in itself should be forward scanning only, with few local variables that would fit into registers nicely.
I could use zlib or 7z api for such task, but I would really want to cook something on my own.
Any ideas?
PNG works this way (approximately): For each pixel, replace its value by the value that it had minus the value of the pixel left to it. Do this from right to left.
Then you can calculate the entropy (bits per character) by making a table of how often which value appears now, making relative values out of these absolute ones and adding the results of log2(n)*n for each element.
Oh, and you have to do this for each color channel (r, g, b) seperately.
For the result, take the average of the bits per character for the channels and divide it by 2^8 (assuming that you have 8 bit per color).
I have an audio file and I am iterating through the file and taking 512 samples at each step and then passing them through an FFT.
I have the data out as a block 514 floats long (Using IPP's ippsFFTFwd_RToCCS_32f_I) with real and imaginary components interleaved.
My problem is what do I do with these complex numbers once i have them? At the moment I'm doing for each value
const float realValue = buffer[(y * 2) + 0];
const float imagValue = buffer[(y * 2) + 1];
const float value = sqrt( (realValue * realValue) + (imagValue * imagValue) );
This gives something slightly usable but I'd rather some way of getting the values out in the range 0 to 1. The problem with he above is that the peaks end up coming back as around 9 or more. This means things get viciously saturated and then there are other parts of the spectrogram that barely shows up despite the fact that they appear to be quite strong when I run the audio through audition's spectrogram. I fully admit I'm not 100% sure what the data returned by the FFT is (Other than that it represents the frequency values of the 512 sample long block I'm passing in). Especially my understanding is lacking on what exactly the compex number represents.
Any advice and help would be much appreciated!
Edit: Just to clarify. My big problem is that the FFT values returned are meaningless without some idea of what the scale is. Can someone point me towards working out that scale?
Edit2: I get really nice looking results by doing the following:
size_t count2 = 0;
size_t max2 = kFFTSize + 2;
while( count2 < max2 )
{
const float realValue = buffer[(count2) + 0];
const float imagValue = buffer[(count2) + 1];
const float value = (log10f( sqrtf( (realValue * realValue) + (imagValue * imagValue) ) * rcpVerticalZoom ) + 1.0f) * 0.5f;
buffer[count2 >> 1] = value;
count2 += 2;
}
To my eye this even looks better than most other spectrogram implementations I have looked at.
Is there anything MAJORLY wrong with what I'm doing?
The usual thing to do to get all of an FFT visible is to take the logarithm of the magnitude.
So, the position of the output buffer tells you what frequency was detected. The magnitude (L2 norm) of the complex number tells you how strong the detected frequency was, and the phase (arctangent) gives you information that is a lot more important in image space than audio space. Because the FFT is discrete, the frequencies run from 0 to the nyquist frequency. In images, the first term (DC) is usually the largest, and so a good candidate for use in normalization if that is your aim. I don't know if that is also true for audio (I doubt it)
For each window of 512 sample, you compute the magnitude of the FFT as you did. Each value represents the magnitude of the corresponding frequency present in the signal.
mag
/\
|
| ! !
| ! ! !
+--!---!----!----!---!--> freq
0 Fs/2 Fs
Now we need to figure out the frequencies.
Since the input signal is of real values, the FFT is symmetric around the middle (Nyquist component) with the first term being the DC component. Knowing the signal sampling frequency Fs, the Nyquist frequency is Fs/2. And therefore for the index k, the corresponding frequency is k*Fs/512
So for each window of length 512, we get the magnitudes at specified frequency. The group of those over consecutive windows form the spectrogram.
Just so people know I've done a LOT of work on this whole problem. The main thing I've discovered is that the FFT requires normalisation after doing it.
To do this you average all the values of your window vector together to get a value somewhat less than 1 (or 1 if you are using a rectangular window). You then divide that number by the number of frequency bins you have post the FFT transform.
Finally you divide the actual number returned by the FFT by the normalisation number. Your amplitude values should now be in the -Inf to 1 range. Log, etc, as you please. You will still be working with a known range.
There are a few things that I think you will find helpful.
The forward FT will tend to give larger numbers in the output than in the input. You can think of it as all of the intensity at a certain frequency being displayed at one place rather than being distributed through the dataset. Does this matter? Probably not because you can always scale the data to fit your needs. I once wrote an integer based FFT/IFFT pair and each pass required rescaling to prevent integer overflow.
The real data that are your input are converted into something that is almost complex. As it turns out buffer[0] and buffer[n/2] are real and independent. There is a good discussion of it here.
The input data are sound intensity values taken over time, equally spaced. They are said to be, appropriately enough, in the time domain. The output of the FT is said to be in the frequency domain because the horizontal axis is frequency. The vertical scale remains intensity. Although it isn't obvious from the input data, there is phase information in the input as well. Although all of the sound is sinusoidal, there is nothing that fixes the phases of the sine waves. This phase information appears in the frequency domain as the phases of the individual complex numbers, but often we don't care about it (and often we do too!). It just depends upon what you are doing. The calculation
const float value = sqrt((realValue * realValue) + (imagValue * imagValue));
retrieves the intensity information but discards the phase information. Taking the logarithm essentially just dampens the big peaks.
Hope this is helpful.
If you are getting strange results then one thing to check is the documentation for the FFT library to see how the output is packed. Some routines use a packed format where real/imaginary values are interleaved, or they may begin at the N/2 element and wrap around.
For a sanity check I would suggest creating sample data with known characteristics, eg Fs/2, Fs/4 (Fs = sample frequency) and compare the output of the FFT routine with what you'd expect. Try creating both a sine and cosine at the same frequency, as these should have the same magnitude in the spectrum, but have different phases (ie the realValue/imagValue will differ, but the sum of squares should be the same.
If you're intending on using the FFT though then you really need to know how it works mathematically, otherwise you're likely to encounter other strange problems such as aliasing.