Visualizing .wav Files in MATLAB: A Comprehensive Guide

Waveform and spectrogram visualization of .wav files is crucial for audio analysis, research, and development. This guide provides a step-by-step approach to visualizing .wav files using MATLAB, including advanced techniques such as FFT parameters and spectrograms.

Step-by-Step Visualization in MATLAB

To visualize a .wav file in MATLAB, you can follow these steps:

1. Read the .wav file

First, use the audioread function to load the audio data and its sample rate. Here is the basic syntax:

[y, Fs] audioread('yourfile.wav');

2. Create a Time Vector

Next, create a time vector based on the sample rate:

t 0:length(y)-1/Fs;

3. Plot the Waveform

Using the plot function, create a waveform plot:

figure();
plot(t, y);
xlabel('Time (seconds)');
ylabel('Amplitude');
title('Waveform of the Audio Signal');
grid on;

4. Handle Stereo Audio

If the audio file is stereo, the y variable has two columns. You can plot each channel separately:

figure();
subplot(2, 1, 1);
plot(t, y(:, 1));
xlabel('Time (seconds)');
ylabel('Amplitude');
title('Left Channel');
grid on;

subplot(2, 1, 2);
plot(t, y(:, 2));
xlabel('Time (seconds)');
ylabel('Amplitude');
title('Right Channel');
grid on;

Advanced Visualization Techniques

1. FFT Parameters

To analyze the frequency components, you can use the Fast Fourier Transform (FFT) with specific parameters. Here is an example of setting FFT parameters:

NFFT 8192;
NOVERLAP round(0.75 * NFFT);
w hanning(NFFT);
spectrogram_dB_scale 100; % dB range scale means the lowest displayed level is XX dB below the max level

2. Spectrogram Visualization

The spectrogram provides a visual representation of the frequency content of the signal over time. Here's how to create a spectrogram:

[sg, fsg, tsg, sgfreq] specgram(signal, NFFT, Fs, NOVERLAP, w);
sg_dB_peak 20 * log10(abs(sg)); % Convert from linear to dB peak

saturation_dB 60; % Saturation of the dB range
min_disp_dB round(max(max(sg_dB_peak)) - spectrogram_dB_scale);
sg_dB_peak(sg_dB_peak
figure();
imagesc(tsg, fsg, sg_dB_peak);
colormap(jet);
axis xy;
colorbar;
grid on;
title([my_title ' / Fs ' num2str(Fs) ' Hz / Delta f ' num2str(fsg(2) - fsg(1)) ' Hz']);
xlabel('Time (s)');
ylabel('Frequency (Hz)');

Weighting Functions

If you are dealing with acoustics, you may wish to have a weighted spectrum. A weighting curve can be applied as follows:

function pondA_dB pondA_function(f)
n 12200^2 * f.^4 ./ (f.^2 20.6^2 * f.^2 12200^2 .*sqrt(f.^2 107.7^2) .* sqrt(f.^2 737.9^2));
r 12200^2 * 1000.^4 ./ (1000.^2 20.6^2 * 1000.^2 12200^2 .* sqrt(1000.^2) 107.7^2 .* sqrt(1000.^2) 737.9^2);
pondA n ./ r;
pondA_dB 20 * log10(pondA);
end

This function applies the A-weighting curve to the spectrogram to provide a more accurate representation of perceived loudness.

Conclusion

By following the steps outlined in this guide, you can effectively visualize and analyze .wav files in MATLAB, enhancing your understanding of the audio signal. Whether you're working on audio processing, signal analysis, or audio engineering, MATLAB offers powerful tools to help you achieve your goals.