Matlab Audio Signal Analysis

In summary, the conversation discusses the process of plotting sound's magnitude vs. frequency using a wav file. The person has managed to read in the file as a matrix and compute the fft, resulting in a symmetric plot with peaks at the start and end. It is explained that when doing fft of real-valued signals, the result is a series of complex conjugates symmetric about zero and taking the magnitude of these conjugates will result in equal values. The person is confused about the plot and seeks help. It is suggested to refer to course materials for more details.
  • #1
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Hi,

I am trying to get a plot of the sound's magnitude vs. frequency. I have managed to read in a wav file as a matrix. I then compute the fft of this matrix. When I plot the absolute value of this, I get a symmetric plot with peaks at the start and the end. What is this a plot of? Is it magnitude vs. frequency? Someone please help, I'm really confused! Any help would be much appreciated!
 
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  • #2
when you do the fft of a real-valued signal, you get a series of complex-valued frequencies where the real and "imaginary" parts are vectors containing the phase information (that is, how much those sinusoids are shifted along the x-axis).

because of the mathematical properties of ffts of real signals, the result is also a series of complex conjugates symmetric about zero. when you take the magnitude of the conjugates, they are obviously equal. so... when you're only interested in magnitude, you only look at half of the result.

if you want details, you could download the course materials here:
http://see.stanford.edu/SEE/courseinfo.aspx?coll=84d174c2-d74f-493d-92ae-c3f45c0ee091 [Broken]
 
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  • #3


Hi there,

It sounds like you are on the right track with your analysis. When you plot the absolute value of the FFT, you are plotting the magnitude vs. frequency. The peaks at the start and end are likely due to the way the FFT algorithm works and can be ignored. To get a clearer plot, you can try using a logarithmic scale for the frequency axis. This will help to spread out the data and make it easier to interpret. Additionally, you can also try applying a window function to your signal before computing the FFT, as this can help to reduce any artifacts in the frequency domain. I hope this helps! Keep up the good work with your audio signal analysis.
 

1. What is Matlab Audio Signal Analysis?

Matlab Audio Signal Analysis is a process in which audio signals are analyzed and processed using the Matlab software. It involves extracting and manipulating features from audio signals to understand and interpret the data.

2. What are the applications of Matlab Audio Signal Analysis?

Matlab Audio Signal Analysis has various applications in fields such as speech recognition, music information retrieval, audio classification, and noise reduction. It is also used in audio processing for multimedia applications and audio signal enhancement.

3. How does Matlab handle audio signals?

Matlab has built-in functions and tools specifically designed for handling audio signals. It can read and write audio files in various formats, visualize signals in time and frequency domains, and perform various analysis and processing techniques on the signals.

4. What are some common techniques used in Matlab Audio Signal Analysis?

Some common techniques used in Matlab Audio Signal Analysis include signal filtering, time and frequency domain analysis, feature extraction, and machine learning algorithms for classification and pattern recognition. Matlab also offers various toolboxes for specific audio analysis tasks such as audio compression and speech processing.

5. Can Matlab Audio Signal Analysis be used for real-time processing?

Yes, Matlab Audio Signal Analysis can be used for real-time processing by using the appropriate hardware and tools. The Matlab real-time workshop allows for the development of algorithms and models that can be deployed on hardware such as DSPs and FPGAs for real-time audio signal processing.

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