I want to perform a cross-correlation of two audio files (which are actually NSData objects). I found a vDSP_convD function in accelerate framework. NSData has a property bytes which returns a pointer to an array of voids - that is the parameter of the filter and signal vector.
I struggled with other parameters. What is the length of these vectors or the length of the result vectors?
I guess:
it's the sum of the filter and signal vector.
Could anyone give me an example of using the vDSP_convD function?
Apple reference to the function is here
Thanks
After reading a book - Learning Core Audio, I have made a demo which demonstrates delay between two audio files. I used new iOS 8 API to get samples from the audio files and a good performance optimization.
Github Project.
a call would look like this:
vDSP_conv ( signal *, signalStride, filter *, filterStride, result*, resultStride, resultLenght, filterLength );
where we have:
signal*: a pointer to the first element of your signal array
signalStride: the lets call it "step size" throug your signal array. 1 is every element, 2 is every second ...
same for filter and result array
length for result and filter array
How long do the arrays have to be?:
As stated in the docs you linked our signal array has to be lenResult + lenFilter - 1 which it is where it gets a little messy. You can find a demonstration of this by Apple here or a shorter answer by SO user Rasman here.
You have to do the zero padding of the signal array by yourself so the vector functions can apply the sliding window without preparation.
Note: You might consider using the Fast-Fourier-Transformation for this, because when you work with audio files i assume, that you have quite some data and there is a significant performance increase from a certain point onwards when using:
FFT -> complex multiplication in frequency domain (which results in a correlation in time domain) -> reverse FFT
here you can find a useful piece of code for this!
Related
So in Lua it's common knowledge that you can use math.randomseed but it's also obvious that math.random sets the seed as well (calling it twice does not return the same result), what does it set it to, and how can I keep track of it, and if it's impossible, please explain why that is so.
This is not a Lua question, but general question on how some RNG algorithm works.
First, Lua don't have their own RNG - they just output you (slightly mangled) value from RNG of underlying C library. Most RNG implementations do not reveal you their inner state, but sometimes you can caclulate it yourself.
For example when you use Lua on Windows, you'll be using LCG-based RNG from MS C library. The numbers you get is a slice of seed, not full value. There are two ways you can deal with that:
If you know how many times you called random, you can just take initial seed value, feed it to your copy of the same algorithm with same constants that are hardcoded in MS library and get exact value of seed.
If you don't, but you can be sure that nobody interferes in between your two calls to random, you can get two generated numbers, and reverse LCG algorithm by shifting bits back to their place. This will leave you with several missing bits (with one more bit thanks to Lua mangling) that you will need to simply bruteforce - just reiterate over all missing bits until your copy of algorithm produces exactly same two "random" numbers you've recorded before. That will be current seed stored inside library's RNG as well. Well programmed solution in Lua can bruteforce this in about 0.2-0.5s on somewhat dated PC - I did it past. Here's example on Crypto.SE talking about this task in more details: Predicting values from a Linear Congruential Generator.
First approach can be used with any other RNG algorithm that doesn't use any real entropy, second with most RNGs that don't mask too much bits in slice to make bruteforcing unreasonable.
Real answer though is: you don't need to keep track of seed at all. What you want is probably something else.
If you set a seed all numbers math.random() generates are pseudo-random (This is always the case as the system will generate a seed by itself).
math.randomseed(4)
print(math.random())
print(math.random())
math.randomseed(4)
print(math.random())
Outputs
0.50827539156303
0.75454387490399
0.50827539156303
So if you reset the seed to the same value you can predict all values that are going to come up to the maximum number of consecutive values that you already generated using that seed.
What the seed does not do is keep the output of math.random() the same. It would be the same if you kept resetting it to the same value.
An analogy as an example
Imagine the random number is an integer between 0 and 9 (instead of a double between 0 and 1).
math.random() could traverse pi's decimals from an arbitrary starting position (default could be system time).
What you do when you use set.seed() is (not literally, this is an analogy as mentioned) set the starting decimals of where in pi you are going to retrieve your numbers.
If you now reset the seed to the same starting position the numbers are going to be the same as the last time you reset the starting position.
You will know the numbers of to the last call, after that you can't be certain anymore.
I'm trying to use butterworth filter. The input data comes from an "index array" module (the data is acquired through DAQ and I want to process the voltage signal which is in an array of waveforms). when I use this filter in a case structure, it doesn't work. yet, when I use the filters in the "waveform conditioning" section, there is no problem. what exactly is the difference between these two types of filters?
a little add on to my problem: the second picture is from when i tried to reassemble the initial combination, and the error happened
You are comparing offline filtering to online filtering.
In LabVIEW, the PtbyPt-VIs are intended to be used in an online setting, that is - iteratively.
For each new sample that is obtained, it would be input directly into the VI. The VI stores the states of the previous iterations to perform the filtering.
The "normal" filter VIs are intended for offline analysis and expects an array containing the full data of the signal.
The following whitepaper explains Point-by-Point-VIs. Note that this paper is quite old, so it should explain the concepts - but might be otherwise outdated.
http://www.ni.com/pdf/manuals/370152b.pdf
If VoltageBuf is an array of consecutive values of the same signal (the one that you want to filter) you only need to connect VoltageBuf directly to the filter.
I want to get the properly rendered projection result from a Stage3D framework that presents something of a 'gray box' interface via its API. It is gray rather than black because I can see this critical snippet of source code:
matrix3D.copyFrom (renderable.getRenderSceneTransform (camera));
matrix3D.append (viewProjection);
The projection rendering technique that perfectly suits my needs comes from a helpful tutorial that works directly with AGAL rather than any particular framework. Its comparable rendering logic snippet looks like this:
cube.mat.copyToMatrix3D (drawMatrix);
drawMatrix.prepend (worldToClip);
So, I believe the correct, general summary of what is going on here is that both pieces of code are setting up the proper combined matrix to be sent to the Vertex Shader where that matrix will be a parameter to the m44 AGAL operation. The general description is that the combined matrix will take us from Object Local Space through Camera View Space to Screen or Clipping Space.
My problem can be summarized as arising from my ignorance of proper matrix operations. I believe my failed attempt to merge the two environments arises precisely because the semantics of prepending one matrix to another is not, and is never intended to be, equivalent to appending that matrix to the other. My request, then, can be summarized in this way. Because I have no control over the calling sequence that the framework will issue, e.g., I must live with an append operation, I can only try to fix things on the side where I prepare the matrix which is to be appended. That code is not black-boxed, but it is too complex for me to know how to change it so that it would meet the interface requirements posed by the framework.
Is there some sequence of inversions, transformations or other manuevers which would let me modify a viewProjection matrix that was designed to be prepended, so that it will turn out right when it is, instead, appended to the Object's World Space coordinates?
I am providing an answer more out of desperation than sure understanding, and still hope I will receive a better answer from those more knowledgeable. From Dunn and Parberry's "3D Math Primer" I learned that "transposing the product of two matrices is the same as taking the product of their transposes in reverse order."
Without being able to understand how to enter text involving superscripts, I am not sure if I can reduce my approach to a helpful mathematical formulation, so I will invent a syntax using functional notation. The equivalency noted by Dunn and Parberry would be something like:
AB = transpose (B) x transpose (A)
That comes close to solving my problem, which problem, to restate, is really just a problem arising out of the fact that I cannot control the behavior of the internal matrix operations in the framework package. I can, however, perform appropriate matrix operations on either side of the workflow from local object coordinates to those required by the GPU Vertex Shader.
I have not completed the test of my solution, which requires the final step to be taken in the AGAL shader, but I have been able to confirm in AS3 that the last 'un-transform' does yield exactly the same combined raw data as the example from the author of the camera with the desired lens properties whose implementation involves prepending rather than appending.
BA = transpose (transpose (A) x transpose (B))
I have also not yet tested to see if these extra calculations are so processing intensive as to reduce my application frame rate beyond what is acceptable, but am pleased at least to be able to confirm that the computations yield the same result.
I am writing a histogram like function which looks at vector data and then puts the elements in predefined "histogram" buckets based on which range they are closest to.
I can obviously do this using if condition but I am trying to improve it using NEON because these are image buffers.
One way to do this would be with VCEQ then VBIT but sadly enough I could not find VBIT in the header of neon. Alternatively I figured I could take the VCEQ results and do an exclusive AND with a vector of 1s and then use VBIF :-) but VBIF is not there either!
Any thoughts here?
Thanks
VBIT, VBIF, and VBSL all do the same operation up to permutation of the sources; you can use the vbsl* intrinsics to get any of the three operations.
I'm using OpenCV to compare two blobs in two images. Suppose I've known
a pair of blobs that are likely to be similar, and I know their indices
in the contour arrays (generated by cvFindContours()), how can I get
access to one contour in a constant time?
The most cumbersome way is to use the link operation (contours=contours->h_next) multiple times, but I wonder if there is a faster way to retrieve one contour in an array.
I use CV_RETR_EXTERNAL and CV_CHAIN_APPROX_NONE in calling cvFindContours().
Thanks!
-J.C.
I think the function cvGetSeqElem does what you want. Quoting the OpenCV docs: "The function has O(1) time complexity assuming that the number of blocks is much smaller than the number of elements." I suppose "blocks" means "contours" in this context.
Also, take a look at cvCvtSeqToArray (link), which copies a sequence to one continuous block of memory.