signal-processing

I have two time series of 3D accelerometer data that have different time bases (clocks started at different times, with some very slight creep during the sampling time), as well as containing many gaps of different size (due to delays associated with writing to separate flash devices). The accelerometers I'm using are the inexpensive GCDC X250-2 . I'm running the accelerometers at their highest gain, so the data has a significant noise floor. The time series each have about 2 million data points (over an hour at 512 samples/sec), and contain about 500 events of interest, where a typical event

Need to find if there is a open source project for audio signal processing, just as we have openCV for image processing. Thanks In my course for the more technical part , we were introduced to signal processing, we had to use Octave with the audio package and gnu plot for plotting. For the fun part of the course used MaxMSP/Jitter which is a commercial project, but there's always PureData which is opensource and the Jitter equivalent would be PD Gem , but just for audio signal processing you should be fine with PureData. In short, if you're into the number crunching bit, Octave should be fine.

I've trying to mix together 2 16bit linear PCM audio streams and I can't seem to overcome the noise issues. I think they are coming from overflow when mixing samples together. I have following function ... short int mix_sample(short int sample1, short int sample2) { return #mixing_algorithm#; } ... and here's what I have tried as #mixing_algorithm# sample1/2 + sample2/2 2*(sample1 + sample2) - 2*(sample1*sample2) - 65535 (sample1 + sample2) - sample1*sample2 (sample1 + sample2) - sample1*sample2 - 65535 (sample1 + sample2) - ((sample1*sample2) >> 0x10) // same as divide by 65535 Some of them

Wikipedia's Wavelet article contains this text: The discrete wavelet transform is also less computationally complex, taking O(N) time as compared to O(N log N) for the fast Fourier transform . This computational advantage is not inherent to the transform, but reflects the choice of a logarithmic division of frequency, in contrast to the equally spaced frequency divisions of the FFT. Does this imply that there's also an FFT-like algorithm that uses a logarithmic division of frequency instead of linear? Is it also O(N)? This would obviously be preferable for a lot of applications. Jason Harrison

I a doing a weird project, and looking to convert some short, simple datagrams to audio - send them over a (physical) radio - then to receive and decode them on another device (think - embedded devices with audio out jack and GSM/GPRS-type radios). (I have to use a physical, existing external radio). Does anyone know of a good, simple software modem library good for such a project? I'm not so concerned about data rate, and would prefer simplicity over functionality. Even something akin to a basic 1200 baud modem would be fantastic. Looking at this more of a learning experience and potential

Update this question was previously titled as " Give me the name of a simple algorithm for signal(sound) pattern detection " My objective is to detect the presence of a given pattern in a noisy signal. I want to detect the presence of a species of insect recording the sounds with a microphone. I have previously recorded the sound of the insect in a digital format. I am not trying to do voice recognition. I am already using convolution between the input signal and the pattern to determine their similarity level. But I think that this technique is more suited to discrete time (i.e. digital

I'm looking for how to draw the sound waves according to music. I want waves like this image here is some discussion about displaying Waves from music WaveForm on IOS rendering a waveform on an iphone audio waveform visualisation with iPhone Github Example Links https://github.com/gyetvan-andras/cocoa-waveform https://github.com/benalavi/waveform https://github.com/JingWZ/WaveView https://github.com/gyetvan-andras/cocoa-waveform https://github.com/ioslovers/ATTabandHoldAudioRecord https://github.com/hezone/ECGWavesPlayer https://github.com/iluvcapra/JHWaveform But not getting any idea about

I'm looking for a book similar to "Introduction to Algorithms" by Thomas Cormen geared towards DSP algorithms. Is there anything as thorough as Cormen on the DSP market? EDIT I should say I'm looking for a book that's analogous to The Joy of Cooking. Disclaimer - I am not familiar with the Cormen book so I'm not quite sure what you're looking for. I'm a huge fan of "A Digital Signal Processing Primer" by Ken Steiglitz . It introduces DSP concepts like sampling, as well as simple filtering implementations, without relying just on math for explanation. Cookbook equivalent: You know how to boil

I'm working with some code that does a Fourier transform (to calculate the cepstrum of an audio sample). Before it computes the Fourier transform, it applies a Hamming window to the sample: for(int i = 0; i < SEGMENTATION_LENGTH;i++){ timeDomain[i] = (float) (( 0.53836 - ( 0.46164 * Math.cos( TWOPI * (double)i / (double)( SEGMENTATION_LENGTH - 1 ) ) ) ) * frameBuffer[i]); } Why is it doing this? I can't find any reason for it to do this in the code, or online. Whenever you do a finite Fourier transform, you're implicitly applying it to an infinitely repeating signal. So, for instance, if the

I'm developing an iOS application with SDK for iOS 5.0 and XCode 4.2 . I want to develop an application that recognize sounds. I see there is an application called Sound Hound that recognize music and tells artist and title. How can I do something similar? I want to compare a sound to an existing sound database. How can I do that? Maybe I can use Fourier Transform . I don't know how to process sounds. Or it could be similar to speech recognition, isn't it? I came across a paper which explains how audio search algorithms work. Here is the link . It was written by one of the developers of Shazam