- #1
JJ91
- 41
- 0
Homework Statement
I would like get an advice whatever the plot of phase (in radians) of the FFT computation of audio file is correct.
Is it not supposed to show a decrease as the frequency increases ?
In summary, the conversation discusses the correct plot of the phase in radians for the FFT computation of an audio file. The speaker asks for advice and clarification on the expected phase and its range. They mention using the "unwrap()" command in Matlab but not getting the desired result. The source of the signal and the reason for expecting a specific phase are also questioned. The conversation concludes with the realization that the phase may not be necessary for the analysis of the audio file.
I would like get an advice whatever the plot of phase (in radians) of the FFT computation of audio file is correct.
Is it not supposed to show a decrease as the frequency increases ?
- #2
mfb
Mentor
- 37,122
- 13,962
Phase relative to what? And why does it go from -2500 to +8500 and not from -pi to pi?
Where does the signal come from, why do you expect some specific phase?
Where does the signal come from, why do you expect some specific phase?
- #3
JJ91
- 41
- 0
Phase relative to what?
Relative to amplitude at specified frequencies
And why does it go from -2500 to +8500 and not from -pi to pi?
The command used unwrap() was supposed to make a range of -pi to +pi, however in MatLab it just doesn't work thus I assume it means -2500 rad to +8500 rad.
Where does the signal come from, why do you expect some specific phase?
The signal comes from the FFT of file audio.wav, I had no idea what I was expecting for "phase" the amplitude however makes clear sense.
Relative to amplitude at specified frequencies
And why does it go from -2500 to +8500 and not from -pi to pi?
The command used unwrap() was supposed to make a range of -pi to +pi, however in MatLab it just doesn't work thus I assume it means -2500 rad to +8500 rad.
Where does the signal come from, why do you expect some specific phase?
The signal comes from the FFT of file audio.wav, I had no idea what I was expecting for "phase" the amplitude however makes clear sense.
- #4
mfb
Mentor
- 37,122
- 13,962
I don't think this makes sense.
Well, where is the difference between a phase of x and x+2n*pi?
Me neither, so why do you care about the phase?
- #5
JJ91
- 41
- 0
It was part of the question for analysis of an audio file. I end up submitting only amplitude component, the graph waveform however was correct, the range wasn't.
What is FFT analysis?
FFT (Fast Fourier Transform) analysis is a mathematical algorithm used to convert a time-domain signal into a frequency-domain representation. It decomposes a complex signal into its individual frequency components, allowing for a better understanding of the signal's characteristics.
Why is FFT analysis important?
FFT analysis is important because it allows for the identification of specific frequency components within a signal. This is especially useful in fields such as signal processing, audio engineering, and image processing, where understanding the frequency components of a signal is crucial for making informed decisions.
How does FFT analysis work?
FFT analysis works by breaking down a signal into a series of smaller, simpler sine waves. These sine waves are then analyzed and combined to reconstruct the original signal. This process is repeated for each frequency component, resulting in a frequency spectrum that displays the amplitude of each component.
What are some applications of FFT analysis?
FFT analysis has many applications, including audio and speech processing, image processing, vibration analysis, and digital filtering. It is also commonly used in fields such as physics, engineering, and neuroscience, to analyze data and extract useful information about the underlying signals.
Are there any limitations to FFT analysis?
Yes, there are some limitations to FFT analysis. It assumes that the signal is stationary, meaning that its characteristics do not change over time. It also requires a discrete, evenly-spaced signal, which may not always be the case in real-world data. Additionally, FFT analysis cannot determine the direction of a signal, only its frequency components.
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