spectralEntropy

Spectral entropy for audio signals and auditory spectrograms

Syntax

entropy = spectralEntropy(x,f)

entropy = spectralEntropy(x,f,Name=Value)

spectralEntropy(___)

Description

entropy = spectralEntropy(x,f) returns the spectral entropy of the signal, x, over time. How the function interprets x depends on the shape of f.

entropy = spectralEntropy(x,f,Name=Value) specifies options using one or more name-value arguments.

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.

Examples

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

spectralEntropy(audioIn,fs)

Spectral Entropy of Frequency-Domain Audio Data

Read in an audio file and then calculate the mel spectrogram using the melSpectrogram function.

[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.

spectralEntropy(s,cf)

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

Create a 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:

1. Read in a frame of audio data.

2. Calculate the spectral entropy for the frame of audio.

3. 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')

Use dsp.AsyncBuffer if

• 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 spectralEntropy.

• You want to calculate the spectral entropy for overlapped data.

Create a 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.

plot(logger.Buffer)
ylabel('Entropy')

Input Arguments

x — Input signal

column vector | matrix | 3-D array

Input signal, specified as a vector, matrix, or 3-D array. How the function interprets x depends on the shape of f.

Data Types: single | double

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 of f:

• If f is a scalar, x is interpreted as a time-domain signal, and f is interpreted as the sample rate. In this case, x must be a real vector or matrix. If x is specified as a matrix, the columns are interpreted as individual channels.

• If f is a vector, x is interpreted as a frequency-domain signal, and f is interpreted as the frequencies, in Hz, corresponding to the rows of x. In this case, x must 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 f.

Data Types: single | double

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is 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.

Example: Window=hamming(256)

Note

The following name-value arguments apply if x is a time-domain signal. If x is a frequency-domain signal, name-value arguments are ignored.

Window — Window applied in time domain

rectwin(round(f*0.03)) (default) | vector

Window applied in the time domain, specified as a real vector. The number of elements in the vector must be in the range [1, size(x,1)]. The number of elements in the vector must also be greater than OverlapLength.

Data Types: single | double

OverlapLength — Number of samples overlapped between adjacent windows

round(f*0.02) (default) | non-negative scalar

Number of samples overlapped between adjacent windows, specified as an integer in the range [0, size(Window,1)).

Data Types: single | double

FFTLength — Number of bins in DFT

numel(Window) (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 Window.

Data Types: single | double

Range — Frequency range (Hz)

[0,f/2] (default) | two-element row vector

Frequency range in Hz, specified as a two-element row vector of increasing real values in the range [0, f/2].

Data Types: single | double

SpectrumType — Spectrum type

"power" (default) | "magnitude"

Spectrum type, specified as "power" or "magnitude":

• "power" –– The spectral entropy is calculated for the one-sided power spectrum.

• "magnitude" –– The spectral entropy is calculated for the one-sided magnitude spectrum.

Data Types: char | string

Output Arguments

entropy — Spectral entropy

scalar | vector | matrix

Spectral entropy, returned as a scalar, vector, or matrix. Each row of entropy corresponds to the spectral entropy of a window of x. Each column of entropy corresponds to an independent channel.

Algorithms

The spectral entropy is calculated as described in [1]:

$$\text{entropy}=\frac{-{\displaystyle \sum _{k=b_1}^{b_2}{s}_k\log \left(s_k\right)}}{\log \left(b_2-b_1\right)}$$

where

• s_k is the spectral value at bin k.

• b₁ and b₂ are the band edges, in bins, over which to calculate the spectral entropy.

References

[1] 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.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Version History

Introduced in R2019a