I am making a system generator model for radar pulse compression using HW Cosimulation of Spartan 6.
On internet there are three research papers which are close to what I want to find.
You can see the models in research paper.
Two are using FIR filter for time domain compression and third one is using FFT for freq domain compression, but I can't complete the whole model and generate FIR or FFT using Core gen.
So can someone help me realize the model please? Otherwise if someone has previously worked on it and can provide me with the model files, that would be really helpful.
PS. I have almost completed the time domain model but I do not know how to find out the coefficients of FIR filter.
Thank You. Anxiously waiting for reply.
The link to three research papers are on another site where i asked the same question. I hope admin do not disapprove.
http://forums.xilinx.com/t5/DSP-Tools/Pulse-Compression/m-p/404871#M7586
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I am new to sci-kit learn. I have viewed the online tutorials but they all seem to leverage existing data (e.g., digits, iris, etc). I need the information on how to process images so that they can be used by scikit learn.
Details of my Study: I have a webcam set up outside my office. It captures all of the traffic on my street that passes in the field of view. I have cropped several hundred images of sedans, trucks and SUV's. The goal is to predict whether a vehicle is one of these categories. I have applied Histogram Oriented Gradients (HOG) to these images which I have attached for your review to see the differences in the categories. This blog will not allow me to post any images but you can see them here https://stats.stackexchange.com/questions/149421/obtaining-a-hog-feature-vector-for-implementation-in-svm-in-python. I posted the same question at this site but no response. This post is the closest answer I have found. Resize HOG feature for Scikit-Learn classifier
I wish to train an SVM classifier based on these images. I understand that there are algorithms that exist in scikit-image that prepares the HOG images for use in scikit-learn. Can someone help me understand this process. I am also grateful for any thoughts based on your experience as to the probability of success of this classification study. I also understand that I need to train the model using a negative images ( ones with no vehicles. How is this done?
I know I am asking a lot but I am surprised no one that I am aware of has done a tutorial on these early steps. It seems like a fairly elementary study.
Hello I'm a Java developer and I'm a part of video on demand website team.
I'm currently doing research on how to implement a back-end component that we are planning to build; the component is expected to automatically generate a meaningful thumbnail representing the content of the videos like the algorithm used in YouTube to generate default thumbnails.
However, I can't seem to find any good open source or payed implementation that can do so, and building the algorithm from scratch is very complicated and needs a lot of time that I don't think the company is willing to invest at the current stage (maybe in the future though)
I would appreciate if someone can refer to any implementation that can help me or even vendors that sell an implementation or a product that can serve my component's objective.
Thanks!
As explained by google research blog:
https://research.googleblog.com/2015/10/improving-youtube-video-thumbnails-with.html
The key component is using a convolutional neural network to predict the score for each sampled frame.
There are so many open sourced CNN implementation like caffe or tensorflow. The only efforts are preparing some training data.
I need to implement algorithm which for input has picture ( jpeg ) and create new picture like output, but only with bodies ( background is removed completely ). Input picture is picture with people from vacation and I need to recognize human bodies and remove background. Can someone suggest me what algorithm to use, what book to buy to learn that algorihms ?
Check this link it will perfectly answer your question of removing the background and performing further processing
neural networks are particuarly useful for this kind of task, but the theory is a universe, if you're doing it from scratch ... that's a lot of work
This is a segmentation problem. In the general case, segmenting images is a hard research problem (I just spent five years doing a doctorate on segmenting greyscale medical images, for example) and the way you go about it is strongly tied to the type of images with which you have to deal. The best advice I can give is to go and read the appropriate literature on segmenting colour images (e.g. use Google Scholar). In terms of books, this one's a good general-purpose introduction to image processing:
http://www.amazon.co.uk/Digital-Image-Processing-Rafael-Gonzalez/dp/0130946508/ref=sr_1_7?ie=UTF8&qid=1326236038&sr=8-7
Searching for "segmenting people in colour images" on Google seems to turn up some good links, incidentally.
I have a question for you: you want to implement this using an algorithm? If so, then it might require a lot of things to be done (provided you are new to the field of image processing).
Otherwise you may try using masking techniques in image editing software like Adobe Photoshop (that would hardly take 15 mins, depending upon how well you know it)
A good book to start with image processing techniques is: "Digital Image Processing" by Gonzalez and Woods; it starts from the basics, and explains stuff in depth.
Still it may take a lot of time to develop an algorithm to do this job. I recommend you use some library for the same. OpenCV(opensource computer vision) is an excellent choice. The library itself comes with demos which include programs for face detection etc. The inbuilt functions provide a variety of features (edge detection/Feature identification and extraction, you may have to use this) Here's the link
http://opencv.willowgarage.com/wiki/
The link provides a lot of reference material that you can make use of! :)
Start with facial recognition software and algorithms; they have been the most refined over the years and as long as all of your bodies have heads, you can use exif data to figure image capture orientation (of course you can't completely rely on that), sample the facial skin to get skin tone ranges, and find the attached body. Anything that is not head and body should be deleted. This process assumes that a person has roughly the same skin tone on their face as their body and the camera flash isn't washing this out. You could grab the flash duration and some other attributes from exif and adjust your ranges accordingly.
A lot of software out there can recognize faces (look at iPhoto for example), so you'll have to use the face as a reference point, along with skin tone, to find your body edges. You result isn't going to be perfect, but as long as your approach is sound, you'll end up with something useful.
And release your software as open source when you're done so I can use it... :)
You can download a free PDF of the book Computer Vision by Richard Szeliski from the author's website. Not only do you have a free book on algorithms, but it's a book that addresses this specific problem.
http://szeliski.org/Book/
You'll see this image at the top of that page of the author's website.
Used copies of the hardcover are available for about $62 if you check addall.com. If you spent some time doing image processing, you'll appreciate having a paper copy of at least one good general reference book.
Its tough but not impossible. I can't give you any code but Peter Norvig had a great talk on the value of data and in the talk he shows how he was able to take a picture of lake and remove all the houses blocking the image and have the lake expanded with boats,etc..
The computer basically learned how lakes look and boats go on lakes and then removed the houses and placed it there. He explains his process(but no code or anything).
Here it is:
Peter Norvig - The Unreasonable Effectiveness of Data
http://www.youtube.com/watch?v=yvDCzhbjYWs
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I want to develop an app for detecting wind according the audio stream.
I need some expert thoughts here, just to give me guide lines or some links, I know this is not easy task but I am planning to put a lot of effort here.
My plan is to detect some common patterns in the stream, and if the values are close to this common patterns of the wind noise I will notify that match is found, if the values are closer to the known pattern great, I can be sure that the wind is detected, if the values doesn't match with the patterns then I guess there is no so much wind....
That is my plan at first, but I need to learn how this things are done. Is there some open project already doing this ? Or is there someone who is doing research on this topics ?
The reason I write on this forum is because I do not know how to google it, the things I found was not I was looking for. I really do not know how to start developing this kind of algorithm.
EDIT 1 :
I tried to record a wind, and when I open the saved audio file for me it was just a bunch of numbers :). I do not even see in what format should I save this, is wave good enough ? Should I use something else, or what if I convert the wind noise audio file in mp3 : is this gonna help with parsing ?
Well I got many questions, that is because I do not know from where to read more about this kind of topic. I tag my question with guidlines so I hope someone will help me.
There must be something that is detectable, cause the wind noise is so common, there must be somehow to detect this, we need only someone to give me tips, someone who is familiar with this topic.
I just came across this post I have recently made a library which can detect wind noise in recordings.
I made a model of wind noise and created a database of examples and then trained a Machine Learning algorithm to detect and meter the wind level in a perceptually weighted way.
The C++/C code is here if it is of use to anyone!
The science for your problem is called "pattern classification", especially the subfield of "audio pattern classification". The task is abstracted as classifying a sound recording into two classes (wind and not wind). You seem to have no strong background in signal processing yet, so let me insert one central warning:
Pattern classification is not as easy as it looks at first. Humans excel at pattern classification. Computers don't.
A good first approach is often to compute the correlation of the Fourier transform of your signal and a sample. Don't know how much that will depend on wind speed, however.
You might want to have a look at the bag-of-frames approach, it was used successfully to classify ambient noise.
As #thiton mentioned this is an example of audio pattern classification.
Main characteristics for wind: it's a shaped (band/hp filtered) white noise with small semi-random fluctuations in amplitude and pitch. At least that's how most synthesizers reproduce it and it sounds quite convincing.
You have to check the spectral content and change in the wavefile, so you'll need FFT. Input format doesn't really matter, but obviously raw material (wav) is better.
Once you got that you should detect that it's close to some kind of colored noise and then perhaps extract series of pitch and amplitude and try to use classic pattern classification algorithm for that data set. I think supervised learning could work here.
This is actually a hard problem to solve.
Assuming you have only a single microphone data. The raw data you get when you open an audio file (time-domain signal) has some, but not a lot of information for this kind of processing. You need to go into the frequency domain using FFTs and look at the statistics of the the frequency bins and use that to build a classifier using SVM or Random Forests.
With all due respect to #Karoly-Horvath, I would also not use any recordings that has undergone compression, such as mp3. Audio compression algorithms always distorts the higher frequencies, which as it turns out, is an important feature in detecting wind now. If possible, get the raw PCM data from a mic.
You also need to make sure your recording is sampled at at least 24kHz so you have information of the signal up to 12kHz.
Finally - the wind shape in the frequency domain is not a simple filtered white noise. The characteristics is that it usually has high energy in the low frequencies (a rumbling type of sound) with sheering and flapping sounds in the high frequencies. The high frequency energy is quite transient, so if your FFT size is too big, you will miss this important feature.
If you have 2 microphone data, then this gets a little bit easier. Wind, when recorded, is a local phenomenon. Sure, in recordings, you can hear the rustling of leaves or the sound of chimes caused by the wind. But that is not wind-noise and should not be filtered out.
The actual annoying wind noise you hear in a recording is the air hitting the membrane of your microphone. That effect is a local event - and can be exploited if you have 2 microphones. It can be exploited because the event is local to each individual mic and is not correlated with the other mic. Of course, where the 2 mics are placed in relations to each other is also important. They have to be reasonably close to each other (say, within 8 inches).
A time-domain correlation can then be used to determine the presence of wind noise. (All the other recorded sound are correlated with each other because the mics are fairly close to each other, so a high correlation means no wind, low correlation means wind). If you are going with this approach, your input audio file need not be uncompressed. A reasonable compression algorithm won't affect this.
I hope this overview helps.
I'm building a rhythm-based game, and facing a lot of problems with rhythm-detection. I receive the current spectrum of a playing song. It looks like a float array with 512 floats. 256 for left and right channel representation. FFT is also available. But I have no idea how to work with that data, I've made some experiments with visualizing, but it gave me very few information. I've googled for some ready algorithms, but there is nothing. Please, can someone help me with, maybe, some references, materials, articles connected with rhythm detection, working with audio spectrum. Code will also be very helpful. Thanks.
Maybe you didn't use the right search-terms. Try to google 'tempo detection' or 'beat detection', together with 'code' or 'algorithm'. There are lots of papers, references, code examples, etc.
Just a few hits:
http://www.cs.princeton.edu/~lieber/cos325/final/
http://www.clear.rice.edu/elec301/Projects01/beat_sync/beatalgo.html
You might want to check out the source and project report for the Dancing Monkeys project. Dancing monkeys automatically generates stepfiles for DDR, and it does so using some rather sophisticated beat detection. It's written in matlab.
You should have a look at the beat spectrum algorithm: http://www.rotorbrain.com/foote/papers/icme2001/icmehtml.htm.
It extracts information about rythm and musical structure by computing the similarity of small samples' spectrograms. It is relatively easy to implement and allows robust information to be retrieved.