Audio signal processing is a fascinating field that involves the manipulation and analysis of audio signals to extract meaningful information. In this blog, we'll explore a challenging signal processing task using MATLAB. The goal is to analyze an audio signal, perform filtering, and visualize the results. Students often ask for such tasks by the Best Signal Processing Assignment Help. 

The Challenge 

We start with an audio signal sampled at 44,100 Hz. The task involves several steps: 

Let's dive into the MATLAB code and unravel the secrets hidden in the audio signal! 

The MATLAB Code 

% Load audio file 

filename = 'your_audio_file.wav'; 

[x, fs] = audioread(filename); 

% Task 1: Plot the time-domain waveform 

t = (0:length(x)-1) / fs; 

figure; 

subplot(3,1,1); 

plot(t, x); 

title('Time-Domain Waveform'); 

xlabel('Time (s)'); 

ylabel('Amplitude'); 

% Task 2: Design a bandpass filter 

passband = [1000, 4000]; 

filterOrder = 100; 

bandpassFilter = designfilt('bandpassiir', 'FilterOrder', filterOrder, 'HalfPowerFrequency1', passband(1), 'HalfPowerFrequency2', passband(2), 'SampleRate', fs); 

% Apply the bandpass filter to the signal 

filteredSignal = filter(bandpassFilter, x); 

% Task 3: Plot the spectrogram of the filtered signal 

windowLength = 512; 

overlap = 256; 

subplot(3,1,2); 

spectrogram(filteredSignal, hamming(windowLength), overlap, windowLength, fs, 'yaxis'); 

title('Spectrogram of Filtered Signal'); 

% Task 4: Identify and mark specific time-frequency regions 

% You can manually analyze the spectrogram and mark regions of interest 

% Example: Mark a rectangle around a specific time-frequency region 

rectangle('Position', [2, passband(1), 1, passband(2) - passband(1)], 'EdgeColor', 'r', 'LineWidth', 2); 

% Add labels and legend 

xlabel('Time (s)'); 

ylabel('Frequency (Hz)'); 

legend('Bandpass Filtered Signal'); 

% Display the original and filtered signals 

subplot(3,1,3); 

plot(t, x, 'b', t, filteredSignal, 'r'); 

title('Original and Filtered Signals'); 

xlabel('Time (s)'); 

ylabel('Amplitude'); 

legend('Original Signal', 'Filtered Signal'); 

% Adjust figure for better visualization 

set(gcf, 'Position', [100, 100, 800, 600]); 

% Optional: You can save the figure if needed 

% saveas(gcf, 'signal_processing_result.png'); 

Breaking Down the Analysis 

Time-Domain Waveform 

The first subplot displays the raw audio signal in the time domain. This provides an initial look at the structure of the audio. 

Bandpass Filtering 

A bandpass filter is designed to focus on frequencies between 1000 Hz and 4000 Hz. This step helps isolate specific components of the signal. 

Spectrogram Analysis 

The second subplot shows the spectrogram of the filtered signal. Spectrograms provide a detailed view of how the frequency content of a signal changes over time. 

Identifying Regions of Interest 

Manually identifying regions of interest in the spectrogram allows us to pinpoint specific events or characteristics in the audio signal. 

Original vs. Filtered Signals 

The third subplot compares the original and filtered signals, offering a visual representation of the impact of the bandpass filter. 

Conclusion