Spectral flatness for audio signals and auditory spectrograms
specifies options using one or more flatness
= spectralFlatness(x
,f
,Name,Value
)Name,Value
pair arguments.
[
returns the spectral arithmetic mean and spectral geometric mean.flatness
,arithmeticMean
,geometricMean
] = spectralFlatness(___)
Read in an audio file, calculate the flatness using default parameters, and then plot the results.
[audioIn,fs] = audioread('Counting-16-44p1-mono-15secs.wav'); flatness = spectralFlatness(audioIn,fs); t = linspace(0,size(audioIn,1)/fs,size(flatness,1)); plot(t,flatness) xlabel('Time (s)') ylabel('Flatness')
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 flatness of the mel spectrogram over time. Plot the results.
flatness = spectralFlatness(s,cf); plot(t,flatness) xlabel('Time (s)') ylabel('Flatness')
Read in an audio file.
[audioIn,fs] = audioread('Counting-16-44p1-mono-15secs.wav');
Calculate the flatness of the power spectrum over time. Calculate the flatness for 50 ms Hamming windows of data with 25 ms overlap. Use the range from 62.5 Hz to fs
/2 for the flatness calculation. Plot the results.
flatness = spectralFlatness(audioIn,fs, ... 'Window',hamming(round(0.05*fs)), ... 'OverlapLength',round(0.025*fs), ... 'Range',[62.5,fs/2]); t = linspace(0,size(audioIn,1)/fs,size(flatness,1)); plot(t,flatness) xlabel('Time (s)') ylabel('Flatness')
Create a dsp.AudioFileReader
object to read in audio data frame-by-frame. Create a dsp.SignalSink
to log the spectral flatness 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 flatness for the frame of audio.
Log the spectral flatness for later plotting.
To calculate the spectral flatness 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(); flatness = spectralFlatness(audioIn,fileReader.SampleRate, ... 'Window',win, ... 'OverlapLength',0); logger(flatness) end plot(logger.Buffer) ylabel('Flatness')
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 spectralFlatness
.
You want to calculate the spectral flatness 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 flatness 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); flatness = spectralFlatness(audioBuffered,fs, ... 'Window',win, ... 'OverlapLength',0); logger(flatness) end end release(fileReader)
Plot the logged data.
plot(logger.Buffer)
ylabel('Flatness')
x
— Input signalInput 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)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
spectrums, 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
Specify optional
comma-separated pairs of Name,Value
arguments. Name
is
the argument name and Value
is the corresponding value.
Name
must appear inside quotes. You can specify several name and value
pair arguments in any order as
Name1,Value1,...,NameN,ValueN
.
'Window',hamming(256)
The following name-value pair arguments apply if x
is a
time-domain signal. If x
is a frequency-domain signal, name-value
pair arguments are ignored.
'Window'
— Window applied in time domainrectwin(round(f
*0.03))
(default) | vectorWindow applied in the time domain, specified as the comma-separated pair
consisting of 'Window'
and a real vector. The number of elements in
the vector must be in the range [1,
size(
]. The number of elements in the
vector must also be greater than x
,1)OverlapLength
.
Data Types: single
| double
'OverlapLength'
— Number of samples overlapped between adjacent windowsround(f
*0.02)
(default) | non-negative scalarNumber of samples overlapped between adjacent windows, specified as the
comma-separated pair consisting of 'OverlapLength'
and an integer
in the range [0, size(
).Window
,1)
Data Types: single
| double
'FFTLength'
— Number of bins in DFTnumel(Window
)
(default) | positive scalar integerNumber of bins used to calculate the DFT of windowed input samples, specified as
the comma-separated pair consisting of 'FFTLength'
and 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 vectorFrequency range in Hz, specified as the comma-separated pair consisting of
'Range'
and 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 the comma-separated pair consisting of
'SpectrumType'
and 'power'
or
'magnitude'
:
'power'
–– The spectral flatness is calculated for the
one-sided power spectrum.
'magnitude'
–– The spectral flatness is calculated for
the one-sided magnitude spectrum.
Data Types: char
| string
flatness
— Spectral flatnessSpectral flatness, returned as a scalar, vector, or matrix. Each row of
flatness
corresponds to the spectral flatness of a window of
x
. Each column of flatness
corresponds to an
independent channel.
arithmeticMean
— Spectral arithmetic meanSpectral arithmetic mean, returned as a scalar, vector, or matrix. Each row of
arithmeticMean
corresponds to the arithmetic mean of the spectrum
of a window of x
. Each column of
arithmeticMean
corresponds to an independent channel.
geometricMean
— Spectral geometric meanSpectral geometric mean, returned as a scalar, vector, or matrix. Each row of
geometricMean
corresponds to the geometric mean of the spectrum
of a window of x
. Each column of geometricMean
corresponds to an independent channel.
The spectral flatness is calculated as described in [1]:
where
sk is the spectral value at bin k.
b1 and b2 are the band edges, in bins, over which to calculate the spectral spread.
[1] Johnston, J.d. "Transform Coding of Audio Signals Using Perceptual Noise Criteria." IEEE Journal on Selected Areas in Communications. Vol. 6, Number 2, 1988, pp. 314–323.
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