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When implementing your spectrogram algorithm, make the following assumptions:

  • use a Hamming window
  • the window length is N=256
  • zero-pad by a factor of 2
  • do not overlap

Here are some things to keep in mind:

  • do not vectorize your code or use MATLAB-specific helper functions that are not available on the tablet (such as zeros() or norm() ), as you want to make porting it to C as straightforward as possible.
  • Retain only half of the FFT output, as it is conjugate symmetric (make sure you know why!)
  • If X = Xr + jXi is a complex number, the magnitude squared operation computes Xr^2 + Xi^2.
  • Because power can vary by orders of magnitude, the Log computation is used to reduce the dynamic range of the spectrogram output; this is useful when visualizing the data.

If your input signal is 8192 samples long, then your spectrogram output can be thought of as a 256 x 32 real-valued matrix. Make sure to understand why. You can then use the image() or imagesc() functions in MATLAB to visualize the data.

Part 4: a c implementation of the spectrogram


Your task is to implement a C version of the spectrogram algorithm that you wrote in Part 3. Here are some guidelines for how to proceed:

  • Remember you are doing block-based processing. Every time process() is called, inBuf has N samples available to be processed.
  • Read Section 2.1 of the FFTW tutorial to understand the data structures and function calls of the FFTW library.
  • Remember that floating point is available on this processor.
  • Use the test vector to verify that intermediate operations are being computed correctly (e.g., multiplication, zero-padding, log function, etc.).
  • For extra credit , implement a scheme that allows for arbitrary overlapping. This may require modifying code in Lab4Activity.java

Scaling the output

The values of outBuf must be between 0.0 and 1.0. This is because the output values are directly mapped to RGB colors, with each color channel being 8 bits. As the spectrogram output is generally not in between 0.0 and 1.0, you will need to find an appropriate mapping.

One possible mapping is to linearly scale and saturate the spectrogram output; you must determine the scaling parameters experimentally by processing real audio data. Here is an outline of one way to do this:

  • Start up the GDB debugger and Resume with all breakpoints disabled.
  • While playing a loud tone (i.e., generate in MATLAB and play out through headphones), set a breakpoint right before your process() function returns.
  • Export the inBuf array to a file. Review Part 2: Exporting Variables to a File if you don't remember how.
  • Repeat this process for noise-only input.
  • Import the two files into MATLAB to determine a suitable dynamic range.
This method also enables you to verify the functional correctness of your C code by exporting the spectrogram output to a file.

Quiz information

Be able to describe the effects of windowing and zero-padding on FFT spectral analysis. Know basic properties of the Fourier transform, DTFT, and DFT. What are the trade-offs between block-based and sample-by-sample processing? Although we did not require you to implement it, understand the effects of overlapping when computing the STFT. Understand the basic Android project structure and the relationship between Java and C programming for Android.

Questions & Answers

find the 15th term of the geometric sequince whose first is 18 and last term of 387
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virgelyn Reply
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kinnecy Reply
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I don't understand what the A with approx sign and the boxed x mean
it think it's written 20/(X-6)^2 so it's 20 divided by X-6 squared
I'm not sure why it wrote it the other way
I got X =-6
ok. so take the square root of both sides, now you have plus or minus the square root of 20= x-6
oops. ignore that.
so you not have an equal sign anywhere in the original equation?
is it a question of log
Commplementary angles
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The answer is neither. The function, 2 = 0 cannot exist. Hence, the function is undefined.
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Differences Between Laspeyres and Paasche Indices
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Prasenjit Reply
At high concentrations (>0.01 M), the relation between absorptivity coefficient and absorbance is no longer linear. This is due to the electrostatic interactions between the quantum dots in close proximity. If the concentration of the solution is high, another effect that is seen is the scattering of light from the large number of quantum dots. This assumption only works at low concentrations of the analyte. Presence of stray light.
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Source:  OpenStax, Ece 420 fall 2013. OpenStax CNX. Sep 26, 2013 Download for free at http://cnx.org/content/col11560/1.3
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