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| gf:
--- Quote from: ebastler on April 02, 2024, 09:16:30 am ---There is obviously no noise at much higher frequencies than the 900 Hz, since the noise is bandwidth-limited to 1 kHz. --- End quote --- In fact, the roll off was only 12dB/octave (2nd order Butterworth) beyond 1kHz. Still the effect of the resulting autocorrelation becomes clearly evident upon horizontal zoom-in. That's what I wanted to demonstrate, and I think that's also what the OP wanted to see (even if it diverges from his intuitive expectations). |
| loop123:
--- Quote from: gf on April 02, 2024, 09:43:04 am --- --- Quote from: nctnico on April 02, 2024, 08:49:57 am ---Regarding the bottom picture: The noise is there but the sampling frequency and/or bandwidth is too low to make noise appear as jagged edges. Run an FFT on both signals (using equal record lengths and sampling frequencies). --- End quote --- It is the same noise in both plots. Therefore the spectrum of the noise floor is the same as well. Flat up to ~1kHz, then rolling off with 12dB/octave. The first plots adds a 50Hz sine wave to the noise, and the 2nd plot adds a 900Hz sine wave to the noise. However, time/div is different in both plots in order to fit 10 signal periods of 50Hz or 900Hz into the screen width. [ The units of the x-axis are samples, at a sample rate of 48kSa/s, in both plots. ] --- End quote --- They have same noises but isnt it the 2nd plot has more clean and more easily resolvable sine waves? doesn this mean it is better to make higher frequency signal to create cleaner sine waves? Please tell me the software you used so I can play with it. |
| nctnico:
--- Quote from: loop123 on April 02, 2024, 10:05:51 am --- --- Quote from: gf on April 02, 2024, 09:43:04 am --- --- Quote from: nctnico on April 02, 2024, 08:49:57 am ---Regarding the bottom picture: The noise is there but the sampling frequency and/or bandwidth is too low to make noise appear as jagged edges. Run an FFT on both signals (using equal record lengths and sampling frequencies). --- End quote --- It is the same noise in both plots. Therefore the spectrum of the noise floor is the same as well. Flat up to ~1kHz, then rolling off with 12dB/octave. The first plots adds a 50Hz sine wave to the noise, and the 2nd plot adds a 900Hz sine wave to the noise. However, time/div is different in both plots in order to fit 10 signal periods of 50Hz or 900Hz into the screen width. [ The units of the x-axis are samples, at a sample rate of 48kSa/s, in both plots. ] --- End quote --- They have same noises but isnt it the 2nd plot has more clean and more easily resolvable sine waves? doesn this mean it is better to make higher frequency signal to create cleaner sine waves? Please tell me the software you used so I can play with it. --- End quote --- The 2nd plot isn't more clean! It has less samples. If you can dump the samples into a file and read it into an audio processing program (like Audacity which is free), you can do an FFT analysis. But make sure record an equal number of samples for each recording if you want to compare. |
| ebastler:
--- Quote from: loop123 on April 02, 2024, 10:05:51 am ---They have same noises but isnt it the 2nd plot has more clean and more easily resolvable sine waves? doesn this mean it is better to make higher frequency signal to create cleaner sine waves? --- End quote --- If you know that your signal has 50 Hz frequency (or if you are only interested in the 50 Hz component), you should send the signal through a narrow bandpass filter before detecting or analyzing it. This will get rid of most of the noise at lower and higher frequencies. 50 Hz specifically is not a great choice in this part of the world, however, because it is the mains frequency -- so background hum with that frequency is very easily picked up. Even if your mains is at 60 Hz, I would suggest to stay further away from that frequency, since very narrow (and steep) bandpass filters are difficult beasts. |
| gf:
--- Quote from: loop123 on April 02, 2024, 10:05:51 am ---Please tell me the software you used so I can play with it. --- End quote --- I prefer to use GNU Octave for mathematical calculations. But it is a programming language, i.e. you need to program the underlying calculations. You don't get these plots out of the box with a few clicks. I'm not sure if it is the right tool for you. You already used Audacity. AFAIK, it contains all building blocks to do it (signal generator, noise generator, filters). |
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