Earlier quoted context omitted.
Yes phase information is really important. Try this: 1. Fourier Transform an Image 2. Set all magnitues the the spectrum to 1.0, but do not change the phase 3. Inverse Transform and look at the result 4. Now try the same, but this time keep the maginutes unchanged but change all phases to 0° Spoiler: When changing all amplitudes the image is still regocognizable, when changing all phases, it is not. See example: [1]…
Phase information is important, but as far as I understand it, it is not important in audio: our ears are insensitive to phase. (Phase is important when combining different sinuses of the same frequency, because the sum of those will be different depending on their relative phase, but that's a different matter and not relevant here.) Changing the phases of the different frequencies will result in a waveform that look…
Harmonics Explorer
51–55 of 55 posts
Re: Harmonics Explorer
#52Re: Harmonics Explorer
#53Earlier quoted context omitted.
That reminds me, I saw a video recently of a choir practice where they sang "brighter" and "darker" based on the conductor's hand position. It was fascinating how the singers could control the brightness of their voice while holding the same note and frequency. When they went bright, it sounded closer to "eee" or "iii". When they went dark, it sounded like "uuu" and "ooo". From that, I learned that the lyrics of a so…
You may find this interesting: https://www.youtube.com/watch?v=vC9Qh709gas EDIT: also this https://www.youtube.com/watch?v=FdldD0-kEcc
Re: Harmonics Explorer
#54Earlier quoted context omitted.
Yes phase information is really important. Try this: 1. Fourier Transform an Image 2. Set all magnitues the the spectrum to 1.0, but do not change the phase 3. Inverse Transform and look at the result 4. Now try the same, but this time keep the maginutes unchanged but change all phases to 0° Spoiler: When changing all amplitudes the image is still regocognizable, when changing all phases, it is not. See example: [1]…
Typically the Nyquist Shannon theorem is stated to say that you need to sample at twice the maximum frequency for complete reproducibility. Applying this to a digital signal, that does indeed work with a discrete Fourier transform. Applying this in the perspective of a sample rate (i.e. *.wav file of a recorded audio signal) does not hold true though in my opinion as the discrete signal has become a scalar, not a vec…
Ofcourse depending on how exactly you want to process your samples it might be convenient to have an even higher sampling rate. And if you know your signal does not contain low frequencies (=not using the full bandwidth) you might get away with even lower sampling rates.
But the general case is: you must sample with a rate strictly greater than twice the highest frequences.
Re: Harmonics Explorer
#55Earlier quoted context omitted.
Is this related to formants? I don’t actually know what that term means but I’ve heard it used in this context
As I understand it, the formant is the difference between the interval from the harmonic series, and the interval using whatever temperment you're in. Piano-notes are in equal temperment. String players, and some wind instruments allow you to play the "in-between" notes selectively (like a true major third, unlike the horrid thing a equal temperment produces) giving you a powerful emotional tool