I am working on ECG signals for human identification.i want to extract features from ecg signal using discrete wavelet transform. how I can extract feature from ecg signal. can anyone help me for extracting features?
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Hey guys I'm a beginner in Audio Analysis and trying to find a library which gives me insights like amplitude, classification of sound, what should detect background noise. I have tried out Paura/pyAudioAnalysis (pAura: Python AUdio Recording and Analysis) which analyzes some of the information for live recording. Is there any good audio analysis library in GitHub ?
There are many. search for DTLN model for audio noise removal on github. DTLN is a pretrained noise removal lite model.
if you're not planning to use any models then try to fix this problem using audio signal processing. use audio features like zero crossing rate for noise/speech activity detection.
This question in this community discuss why in speech recognition, the front end generally does signal processing to allow feature extraction from the audio stream. It is nicely explained there,why to prefer DCT over DFT in the second step. But as this process is hardware based implies that there will be some standard circuits for DFT/DCT transform.
when I googled up for FFT algorithm I found some nice material here. But in my current project I need to use DCT. Can someone please point to any standard DCT algorithm/chip which can be used for feature extraction for speech signal?
Currently I am using an accelerometer, Gyro and magnetometer for motion tracking application.I have a 9D sensor fusion functionality to calculate the orientations and gravity cancellation from accelerometer data. How do i now calculate the position of the object in three dimensions? Kindly suggest any algorithm which could give good accuracy.
Extended Kalman filter can give you the best results for motion tracking if you are working on real time application. I would suggest you to refer a book Multi Sensor Data fusion with MATLAB (CRC Press).
I'm currently working on algorithm for denoising images. I need to compare my algorithm with other, existing algorithms, but to do so I need a good quality measure.
The main goal for such measure is to be close to human perception skills. I know, that it is almost impossible, but there are good perceptual measures for audio signals. Are there any similar algorithms for images?
Right now I'm using simple measures, such as mean squared error (MSE), signal to noise ratio (SNR) etc...
And for your information, I'm currently implementing this in matlab
Have you check out SSIM (http://en.wikipedia.org/wiki/Structural_similarity) ? The following page (http://www.ece.uwaterloo.ca/~z70wang/research/ssim/) gives you a real example on how SSIM works compared to MSE.
For example: What algorithm is used to generate the image by the fresco filter in Adobe Photoshop?
Do you know some place where I can read about the algorithms implemented in these filters?
Lode's Computer Graphics Tutorial
The source code for GIMP would be a good place to start. If the code for some filter doesn't make sense, at least you'll find jargon in the code and comments that can be googled.
The Photoshop algorithms can get very complex, and beyond simple blurring and sharpening, each one is a topic unto itself.
For the fresco filter, you might want to start with an SO question on how to cartoon-ify and image.
I'd love to read a collection of the more interesting algorithms, but I don't know of such a compilation.
Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal distortion during processing. Since images are defined over two dimensions (perhaps more) digital image processing may be modeled in the form of multidimensional systems.
Digital image processing allows the use of much more complex algorithms, and hence, can offer both more sophisticated performance at simple tasks, and the implementation of methods which would be impossible by analog means.
In particular, digital image processing is the only practical technology for:
Classification
Feature extraction
Pattern recognition
Projection
Multi-scale signal analysis
Some techniques which are used in digital image processing include:
Pixelation,
Linear filtering,
Principal components analysis
Independent component analysis
Hidden Markov models
Anisotropic diffusion
Partial differential equations
Self-organizing maps
Neural networks
Wavelets