This example shows how to compress the dynamic range of a signal by modifying the range of the magnitude at each frequency bin. This nonlinear spectral modification is followed by an overlap-add FFT algorithm for reconstruction. This system might be used as a speech enhancement system for the hearing impaired. The algorithm in this simulation is derived from a patented system for adaptive processing of telephone voice signals for the hearing impaired originally developed by Alvin M. Terry and Thomas P. Krauss at US West Advanced Technologies Inc., US patent number 5,388,185.
This system decomposes the input signal into overlapping sections of length 256. The overlap is 192 so that every 64 samples, a new section is defined and a new FFT is computed. After the spectrum is modified and the inverse FFT is computed, the overlapping parts of the sections are added together. If no spectral modification is performed, the output is a scaled replica of the input. A reference for the overlap-add method used for the audio signal reconstruction is Rabiner, L. R. and R. W. Schafer. Digital Processing of Speech Signals. Englewood Cliffs, NJ: Prentice Hall, 1978, pgs. 274-277.
Compression maps the dynamic range of the magnitude at each frequency bin from the range 0 to 100 dB to the range ymin to ymax dB. ymin and ymax are vectors in the MATLAB® workspace with one element for each frequency bin; in this case 256. The phase is not altered. This is a non-linear spectral modification. By compressing the dynamic range at certain frequencies, the listener should be able to perceive quieter sounds without being blasted out when they get loud, as in linear equalization.
To use this system to demonstrate frequency-dependent dynamic range compression, start the simulation. After repositioning the input and output figures so you can see them at the same time, change the Slider Gain from 1 to 1000 to 10000. Notice the relative heights of the output peaks change as you increase the magnitude.