Spectral entropy for audio signals and auditory spectrograms
spectralEntropy(___) with no output arguments plots the
spectral entropy. You can specify an input combination from any of the previous syntaxes.
If the input is in the time domain, the spectral entropy is plotted against time.
If the input is in the frequency domain, the spectral entropy is plotted against frame number.
Spectral Entropy of Time-Domain Audio
Read in an audio file and calculate the entropy using default parameters.
[audioIn,fs] = audioread("Counting-16-44p1-mono-15secs.wav"); entropy = spectralEntropy(audioIn,fs);
Plot the spectral entropy against time
Spectral Entropy of Frequency-Domain Audio Data
Read in an audio file and then calculate the mel spectrogram using the
[audioIn,fs] = audioread("Counting-16-44p1-mono-15secs.wav"); [s,cf,t] = melSpectrogram(audioIn,fs);
Calculate the entropy of the mel spectrogram over time.
entropy = spectralEntropy(s,cf);
Plot the spectral entropy against the frame number.
Specify Nondefault Parameters
Read in an audio file.
[audioIn,fs] = audioread("Counting-16-44p1-mono-15secs.wav");
Calculate the entropy of the power spectrum over time. Calculate the entropy for 50 ms Hamming windows of data with 25 ms overlap. Use the range from 62.5 Hz to
fs/2 for the entropy calculation.
entropy = spectralEntropy(audioIn,fs, ... Window=hamming(round(0.05*fs)), ... OverlapLength=round(0.025*fs), ... Range=[62.5,fs/2]);
Plot the spectral entropy against time.
spectralEntropy(audioIn,fs, ... Window=hamming(round(0.05*fs)), ... OverlapLength=round(0.025*fs), ... Range=[62.5,fs/2])
Calculate Spectral Entropy of Streaming Audio
dsp.AudioFileReader object to read in audio data frame-by-frame. Create a
dsp.SignalSink to log the spectral entropy calculation.
fileReader = dsp.AudioFileReader('Counting-16-44p1-mono-15secs.wav'); logger = dsp.SignalSink;
In an audio stream loop:
Read in a frame of audio data.
Calculate the spectral entropy for the frame of audio.
Log the spectral entropy for later plotting.
To calculate the spectral entropy for only a given input frame, specify a window with the same number of samples as the input, and set the overlap length to zero. Plot the logged data.
while ~isDone(fileReader) audioIn = fileReader(); entropy = spectralEntropy(audioIn,fileReader.SampleRate, ... 'Window',hamming(size(audioIn,1)), ... 'OverlapLength',0); logger(entropy) end plot(logger.Buffer) ylabel('Entropy')
The input to your audio stream loop has a variable samples-per-frame.
The input to your audio stream loop has an inconsistent samples-per-frame with the analysis window of
You want to calculate the spectral entropy for overlapped data.
dsp.AsyncBuffer object, reset the logger, and release the file reader.
buff = dsp.AsyncBuffer; reset(logger) release(fileReader)
Specify that the spectral entropy is calculated for 50 ms frames with a 25 ms overlap.
fs = fileReader.SampleRate; samplesPerFrame = round(fs*0.05); samplesOverlap = round(fs*0.025); samplesPerHop = samplesPerFrame - samplesOverlap; win = hamming(samplesPerFrame); while ~isDone(fileReader) audioIn = fileReader(); write(buff,audioIn); while buff.NumUnreadSamples >= samplesPerHop audioBuffered = read(buff,samplesPerFrame,samplesOverlap); entropy = spectralEntropy(audioBuffered,fs, ... 'Window',win, ... 'OverlapLength',0); logger(entropy) end end release(fileReader)
Plot the logged data.
x — Input signal
column vector | matrix | 3-D array
Input signal, specified as a vector, matrix, or 3-D array. How the function
x depends on the shape of
f — Sample rate or frequency vector (Hz)
scalar | vector
Sample rate or frequency vector in Hz, specified as a scalar or vector,
respectively. How the function interprets
x depends on the shape
fis a scalar,
xis interpreted as a time-domain signal, and
fis interpreted as the sample rate. In this case,
xmust be a real vector or matrix. If
xis specified as a matrix, the columns are interpreted as individual channels.
fis a vector,
xis interpreted as a frequency-domain signal, and
fis interpreted as the frequencies, in Hz, corresponding to the rows of
x. In this case,
xmust be a real L-by-M-by-N array, where L is the number of spectral values at given frequencies of
f, M is the number of individual spectra, and N is the number of channels.
The number of rows of
x, L, must be equal to the number of elements of
Specify optional pairs of arguments as
the argument name and
Value is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Before R2021a, use commas to separate each name and value, and enclose
Name in quotes.
The following name-value arguments apply if
x is a time-domain
x is a frequency-domain signal, name-value arguments are
Window — Window applied in time domain
rectwin(round( (default) | vector
OverlapLength — Number of samples overlapped between adjacent windows
round( (default) | non-negative scalar
Number of samples overlapped between adjacent windows, specified as an integer in
the range [0,
FFTLength — Number of bins in DFT
numel( (default) | positive scalar integer
Number of bins used to calculate the DFT of windowed input samples, specified as a
positive scalar integer. If unspecified,
FFTLength defaults to
the number of elements in the
Range — Frequency range (Hz)
[0, (default) | two-element row vector
Frequency range in Hz, specified as a two-element row vector of increasing real
values in the range [0,
SpectrumType — Spectrum type
"power" (default) |
Spectrum type, specified as
"power"–– The spectral entropy is calculated for the one-sided power spectrum.
"magnitude"–– The spectral entropy is calculated for the one-sided magnitude spectrum.
The spectral entropy is calculated as described in :
sk is the spectral value at bin k.
b1 and b2 are the band edges, in bins, over which to calculate the spectral entropy.
 Misra, H., S. Ikbal, H. Bourlard, and H. Hermansky. "Spectral Entropy Based Feature for Robust ASR." 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.
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