Terminator3
Advanced Member level 3
I want to recognize sinusoidal-like signals with >10000 samples in it. It can be done with FFT, but noise performance is unsatisfactionary, so thresholding is difficult on FFT spectrogram. I want to use correlation, but afraid it can become too slow.
I came up with a formula that approximates required signal pretty well, changing parameters in formula i can make few hundrends of waveforms that can be correlated with input signal.
Signal already decimated few times, further decimation leads to aliasing. Is there any way to shrink 10000 samples further? For example, signal uses up to 10kHz frequency, any way of "shrinking" it's frequency range to 5kHz, then decimate?
I read papers about birds singing recognition, but their's approach not effective in my case, as i know very close approximation formula. Although noise is very high too.
I came up with a formula that approximates required signal pretty well, changing parameters in formula i can make few hundrends of waveforms that can be correlated with input signal.
Signal already decimated few times, further decimation leads to aliasing. Is there any way to shrink 10000 samples further? For example, signal uses up to 10kHz frequency, any way of "shrinking" it's frequency range to 5kHz, then decimate?
I read papers about birds singing recognition, but their's approach not effective in my case, as i know very close approximation formula. Although noise is very high too.