melSpectrogram(___) plots the mel spectrogram on a
surface in the current figure.
Use the default settings to calculate the mel spectrogram for an entire audio file. Print the number of bandpass filters in the filter bank and the number of frames in the mel spectrogram.
[audioIn,fs] = audioread('Counting-16-44p1-mono-15secs.wav'); S = melSpectrogram(audioIn,fs); [numBands,numFrames] = size(S); fprintf("Number of bandpass filters in filterbank: %d\n",numBands)
Number of bandpass filters in filterbank: 32
fprintf("Number of frames in spectrogram: %d\n",numFrames)
Number of frames in spectrogram: 1551
Plot the mel spectrogram.
Calculate the mel spectrums of 2048-point windows with 1024-point overlap. Convert to the frequency domain using a 4096-point FFT. Pass the frequency-domain representation through 64 half-overlapped triangular bandpass filters that span the range 62.5 Hz to 8 kHz.
[audioIn,fs] = audioread('FunkyDrums-44p1-stereo-25secs.mp3'); S = melSpectrogram(audioIn,fs, ... 'WindowLength',2048,... 'OverlapLength',1024, ... 'FFTLength',4096, ... 'NumBands',64, ... 'FrequencyRange',[62.5,8e3]);
melSpectrogram again, this time with no output arguments so that you can visualize the mel spectrogram. The input audio is a multichannel signal. If you call
melSpectrogram with a multichannel input and with no output arguments, only the first channel is plotted.
melSpectrogram(audioIn,fs, ... 'WindowLength',2048,... 'OverlapLength',1024, ... 'FFTLength',4096, ... 'NumBands',64, ... 'FrequencyRange',[62.5,8e3])
melSpectrogram applies a frequency-domain filter bank to audio signals that are windowed in time. You can get the center frequencies of the filters and the time instants corresponding to the analysis windows as the second and third output arguments from
Get the mel spectrogram, filter bank center frequencies, and analysis window time instants of a multichannel audio signal. Use the center frequencies and time instants to plot the mel spectrogram for each channel.
[audioIn,fs] = audioread('AudioArray-16-16-4channels-20secs.wav'); [S,cF,t] = melSpectrogram(audioIn,fs); S = 10*log10(S+eps); % Convert to dB for plotting for i = 1:size(S,3) figure(i) surf(t,cF,S(:,:,i),'EdgeColor','none'); xlabel('Time (s)') ylabel('Frequency (Hz)') view([0,90]) title(sprintf('Channel %d',i)) axis([t(1) t(end) cF(1) cF(end)]) end
audioIn— Audio input
Audio input, specified as a column vector or matrix. If specified as a matrix, the function treats columns as independent audio channels.
fs— Input sample rate (Hz)
Input sample rate in Hz, specified as a positive scalar.
comma-separated pairs of
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
'WindowLength'— Analysis window length (samples)
round(0.03*(default) | integer in the range
Analysis window length in samples, specified as the comma-separated pair
'WindowLength' and an integer in the range
'OverlapLength'— Analysis window overlap length (samples)
round(0.02*(default) | integer in the range
Analysis window overlap length in samples, specified as the comma-separated pair
'OverlapLength' and an integer in the range
WindowLength - 1)]
'FFTLength'— Number of DFT points
WindowLength(default) | positive integer
Number of points used to calculate the DFT, specified as the comma-separated pair
'FFTLength' and a positive integer greater than or
WindowLength. If unspecified,
FFTLength defaults to
'NumBands'— Number of mel bandpass filters
32(default) | positive integer
Number of mel bandpass filters, specified as the comma-separated pair consisting
'NumBands' and a positive integer.
'FrequencyRange'— Frequency range over which to compute mel spectrogram (Hz)
[0(default) | two-element row vector
Frequency range over which to compute the mel spectrogram in Hz, specified as the
comma-separated pair consisting of
'FrequencyRange' and a
two-element row vector of monotonically increasing values in the range
'SpectrumType'— Type of mel spectrogram
Type of mel spectrogram, specified as the comma-separated pair consisting of
S— Mel spectrogram
Mel spectrogram, returned as a column vector, matrix, or 3-D array. The dimensions
Trailing singleton dimensions are removed from the output
F— Center frequencies of mel bandpass filters (Hz)
Center frequencies of mel bandpass filters in Hz, returned as a row vector with
T— Location of each window of audio (s)
Location of each analysis window of audio in seconds, returned as a row vector
size(. The location corresponds to
the center of each window.
melSpectrogram function follows the general algorithm to compute
a mel spectrogram as described in .
In this algorithm, the audio input is first buffered into frames of
WindowLength number of samples. The frames are overlapped by
OverlapLength number of samples. A periodic
hamming window is applied to each frame, and then the frame is
converted to frequency-domain representation with
FFTLength number of
points. The frequency-domain representation can be either magnitude or power, specified by
SpectrumType. Each frame of the frequency-domain representation passes
through a mel filter bank. The spectral values output from the mel filter bank are summed, and
then the channels are concatenated so that each frame is transformed to a
NumBands-element column vector.
The mel filter bank is designed as half-overlapped triangular filters equally spaced on
the mel scale.
NumBands controls the number of mel bandpass filters.
FrequencyRange controls the band edges of the first and last filters
in the mel filter bank. The filters are normalized by their bandwidths, so that if white
noise is input to the system, each filter outputs an equal amount of energy.
 Rabiner, Lawrence R., and Ronald W. Schafer. Theory and Applications of Digital Speech Processing. Upper Saddle River, NJ: Pearson, 2010.