melSpectrogram

Mel spectrogram

Syntax

S = melSpectrogram(audioIn,fs)

S = melSpectrogram(audioIn,fs,Name,Value)

[S,F,T] = melSpectrogram(___)

melSpectrogram(___)

Description

S = melSpectrogram(audioIn,fs) returns the mel spectrogram of the audio input at sample rate fs. The function treats columns of the input as individual channels.

S = melSpectrogram(audioIn,fs,Name,Value) specifies options using one or more Name,Value pair arguments.

[S,F,T] = melSpectrogram(___) returns the center frequencies of the bands in Hz and the location of each window of data in seconds. The location corresponds to the center of each window. You can use this output syntax with any of the previous input syntaxes.

melSpectrogram(___) plots the mel spectrogram on a surface in the current figure.

Examples

Calculate Mel Spectrogram

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.

melSpectrogram(audioIn,fs)

Calculate Mel Spectrums of 2048-Point Windows

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]);

Call 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])

Get Filter Bank Center Frequencies and Analysis Window Time Instants

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 melSpectrogram.

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

Input Arguments

audioIn — Audio input

column vector | matrix

Audio input, specified as a column vector or matrix. If specified as a matrix, the function treats columns as independent audio channels.

Data Types: single | double

fs — Input sample rate (Hz)

positive scalar

Input sample rate in Hz, specified as a positive scalar.

Data Types: single | double

Name-Value Pair Arguments

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.

Example: 'WindowLength',1024

'WindowLength' — Analysis window length (samples)

round(0.03*fs) (default) | integer in the range [2, size(audioIn,1)]

Analysis window length in samples, specified as the comma-separated pair consisting of 'WindowLength' and an integer in the range [2, size(audioIn,1)].

Data Types: single | double

'OverlapLength' — Analysis window overlap length (samples)

round(0.02*fs) (default) | integer in the range [0, (WindowLength - 1)]

Analysis window overlap length in samples, specified as the comma-separated pair consisting of 'OverlapLength' and an integer in the range [0, (WindowLength - 1)].

Data Types: single | double

'FFTLength' — Number of DFT points

WindowLength (default) | positive integer

Number of points used to calculate the DFT, specified as the comma-separated pair consisting of 'FFTLength' and a positive integer greater than or equal to WindowLength. If unspecified, FFTLength defaults to WindowLength.

Data Types: single | double

'NumBands' — Number of mel bandpass filters

32 (default) | positive integer

Number of mel bandpass filters, specified as the comma-separated pair consisting of 'NumBands' and a positive integer.

Data Types: single | double

'FrequencyRange' — Frequency range over which to compute mel spectrogram (Hz)

[0 fs/2] (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 [0, fs/2].

Data Types: single | double

'SpectrumType' — Type of mel spectrogram

'power' (default) | 'magnitude'

Type of mel spectrogram, specified as the comma-separated pair consisting of 'SpectrumType' and 'power' or 'magnitude'.

Data Types: char | string

Output Arguments

S — Mel spectrogram

column vector | matrix | 3-D array

Mel spectrogram, returned as a column vector, matrix, or 3-D array. The dimensions of S are L-by-M-by-N, where:

• L is the number of frequency bins in each mel spectrum. NumBands and fs determine L.

• M is the number of frames the audio signal is partitioned into. size(audioIn,1), WindowLength, and OverlapLength determine M.

• N is the number of channels such that N = size(audioIn,2).

Trailing singleton dimensions are removed from the output S.

Data Types: single | double

F — Center frequencies of mel bandpass filters (Hz)

row vector

Center frequencies of mel bandpass filters in Hz, returned as a row vector with length size(S,1).

Data Types: single | double

T — Location of each window of audio (s)

row vector

Location of each analysis window of audio in seconds, returned as a row vector length size(S,2). The location corresponds to the center of each window.

Data Types: single | double

Algorithms

The melSpectrogram function follows the general algorithm to compute a mel spectrogram as described in [1].

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.

Filter Bank Design

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.

References

[1] Rabiner, Lawrence R., and Ronald W. Schafer. Theory and Applications of Digital Speech Processing. Upper Saddle River, NJ: Pearson, 2010.

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™.