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spectralSkewness

Spectral skewness for audio signals and auditory spectrograms

Description

example

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

example

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

[skewness,spread,centroid] = spectralSkewness(___) returns the spectral spread and spectral centroid. You can specify an input combination from any of the previous syntaxes.

example

spectralSkewness(___) with no output arguments plots the spectral skewness.

  • If the input is in the time domain, the spectral skewness is plotted against time.

  • If the input is in the frequency domain, the spectral skewness is plotted against frame number.

Examples

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Read in an audio file, calculate the skewness using default parameters.

[audioIn,fs] = audioread("Counting-16-44p1-mono-15secs.wav");
skewness = spectralSkewness(audioIn,fs);

Plot the spectral skewness against time.

spectralSkewness(audioIn,fs)

Figure contains an axes object. The axes object contains an object of type line.

Read in an audio file and then calculate the mel spectrogram using the melSpectrogram function. Calculate the skewness of the mel spectrogram over time.

[audioIn,fs] = audioread("Counting-16-44p1-mono-15secs.wav");

[s,cf,t] = melSpectrogram(audioIn,fs);

skewness = spectralSkewness(s,cf);

Plot the spectral skewness against the frame number.

spectralSkewness(s,cf)

Read in an audio file.

[audioIn,fs] = audioread("Counting-16-44p1-mono-15secs.wav");

Calculate the skewness of the power spectrum over time. Calculate the skewness for 50 ms Hamming windows of data with 25 ms overlap. Use the range from 62.5 Hz to fs/2 for the skewness calculation.

skewness = spectralSkewness(audioIn,fs, ...
                    Window=hamming(round(0.05*fs)), ...
                    OverlapLength=round(0.025*fs), ...
                    Range=[62.5,fs/2]);

Plot the spectral skewness.

spectralSkewness(audioIn,fs, ...
                 Window=hamming(round(0.05*fs)), ...
                 OverlapLength=round(0.025*fs), ...
                 Range=[62.5,fs/2])

Create a dsp.AudioFileReader object to read in audio data frame-by-frame. Create a dsp.SignalSink to log the spectral skewness 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 skewness for the frame of audio.

  3. Log the spectral skewness for later plotting.

To calculate the spectral skewness 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.

win = hamming(fileReader.SamplesPerFrame);
while ~isDone(fileReader)
    audioIn = fileReader();
    skewness = spectralSkewness(audioIn,fileReader.SampleRate, ...
                               'Window',win, ...
                               'OverlapLength',0);
    logger(skewness)
end

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

Figure contains an axes object. The axes object contains an object of type line.

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 spectralSkewness.

  • You want to calculate the spectral skewness 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 skewness 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);
        
        skewness = spectralSkewness(audioBuffered,fs, ...
                                   'Window',win, ...
                                   'OverlapLength',0);
        logger(skewness)
    end
    
end
release(fileReader)

Plot the logged data.

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

Figure contains an axes object. The axes object contains an object of type line.

Input Arguments

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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

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 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

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

Data Types: single | double

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

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

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

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

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

Data Types: char | string

Output Arguments

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

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

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

Algorithms

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

skewness=k=b1b2(fkμ1)3sk(μ2)3k=b1b2sk

where

  • fk is the frequency in Hz corresponding to bin k.

  • sk is the spectral value at bin k.

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

  • μ1 is the spectral centroid, calculated as described by the spectralCentroid function.

  • μ2 is the spectral spread, calculated as described by the spectralSpread function.

References

[1] Peeters, G. "A Large Set of Audio Features for Sound Description (Similarity and Classification) in the CUIDADO Project." Technical Report; IRCAM: Paris, France, 2004.

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