Understanding the Fourier Transform for Sound Resynthesis

In summary, the conversation discusses using an audio recording and Fourier Transform to resynthesize a sound using sine wave oscillators. The question is whether the raw output of the FFT can provide the relative amplitude information needed, and the suggestion is to consult the comp.dsp USENET group or music-dsp mailing list for help. The answer is that the FFT bins can provide some information, but it may not be exact due to windowing effects and smearing of frequencies. The conversation also mentions potential fights on the USENET group about the nature of the Discrete Fourier Transform and the indexing base in MATLAB.
  • #1
max_planck735
5
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I want to take an audio recording of a sound, perform a Fourier Transform on this sound, and then use the amplitude/frequency/phase information provided by this transform to set the amplitude/frequency/phase of an set of sine wave oscillators, in order to resynthesize the sound.

I need to know the relative amplitudes of the sinusoidal components of the sound in order to set the relative amplitudes of my set of sine wave oscillators properly.

Will the raw output of the FFT provide the relative amplitude information that I'm looking for, if I plot the real number parts against an appropriate frequency axis and read the relative amplitudes of the peaks from this spectrum? If not, should I be plotting the absolute value of the FFT's output in order to obtain the relative amplitude information that I'm looking for? If not either of these, then can anyone suggest what would work?

Thanks, any help is very much appreciated
 
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  • #2
max_planck735 said:
Will the raw output of the FFT provide the relative amplitude information that I'm looking for, if I plot the real number parts against an appropriate frequency axis and read the relative amplitudes of the peaks from this spectrum? If not, should I be plotting the absolute value of the FFT's output in order to obtain the relative amplitude information that I'm looking for? If not either of these, then can anyone suggest what would work?

this is good question to take to the comp.dsp USENET group and/or the music-dsp mailing list (you can go to http://shoko.calarts.edu/~glmrboy/musicdsp/music-dsp.html to subscribe).

because of windowing effects and smearing of frequency components, the FFT bins will have something to do with the amplitude of the sinusoidal components but it won't be exact. if you assume that the spectral smearing of any frequency component has only neglegibly leaked into the bins of all other frequency components, then the answer would be "yes, you can read the relative amplitudes (and frequency and phase) of the sinusoidal components from the peaks in the spectrum."
 
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  • #3
Thanks a lot for the info, especially about the Usenet group/mailing list, that will be very helpful for me. Thanks! =)
 
  • #4
max_planck735 said:
Thanks a lot for the info, especially about the Usenet group/mailing list, that will be very helpful for me. Thanks! =)

yer welcome. you might recognize me there at comp.dsp or (less often) at the music-dsp mailing list. i sometimes get in fights about the periodic or circular nature of the Discrete Fourier Transform (and where, in the signal path, any windowing happens to that gets applied to the DFT) - I'm in that fight right now. other tiffs had to do with the nature of the dirac delta "function" and the necessary scaling in the Nyquist/Shannon sampling and reconstruction theorem.

oh, and another big fight i once was in on comp.dsp and comp.soft-sys.matlab was about the horrible, awful, fixed-forever-and-can-never-change-it, indexing base in MATLAB. all array indices must start at "1" which makes things very ugly in the DSP world.i try to tread more lightly here, since i am not a physicist.
 
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Related to Understanding the Fourier Transform for Sound Resynthesis

1. What is the Fourier Transform?

The Fourier Transform is a mathematical operation that decomposes a signal into its individual frequency components. It allows us to analyze a signal in the frequency domain, where we can see the contribution of each frequency to the overall signal.

2. Why is the Fourier Transform important?

The Fourier Transform is important because it is a powerful tool for analyzing signals and systems in various fields such as engineering, physics, and mathematics. It allows us to understand the behavior of a signal in terms of its frequency components, which can provide insights into the underlying physical processes.

3. What is the difference between the Fourier Transform and the Fourier Series?

The Fourier Transform is used for continuous signals, while the Fourier Series is used for periodic signals. The Fourier Transform gives us a continuous spectrum of frequencies, while the Fourier Series gives us a discrete set of frequencies. Additionally, the Fourier Transform is defined for both real and complex signals, while the Fourier Series is only defined for real signals.

4. How is the Fourier Transform calculated?

The Fourier Transform is calculated by taking the integral of the signal multiplied by a complex exponential over all time. This can be done analytically or numerically using various techniques such as the Fast Fourier Transform (FFT).

5. What are some applications of the Fourier Transform?

The Fourier Transform has a wide range of applications, including signal processing, image and audio compression, spectral analysis, and solving differential equations. It is also used in various fields such as astronomy, medical imaging, and quantum mechanics.

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