Speech and Audio Signal Processing in MATLAB: From Basics to Projects

Speech and audio signal processing is an exciting area that seems to be very important nowadays from virtual assistants to voice-controlled gadgets, music analysis, and systems for speech recognition. As the applications of sound become important, the knowledge of handling signals becomes the greatest asset.
 
MATLAB is an extremely powerful yet easy-to-use platform that can be used for the analysis, processing, and interpretation of signals. If you are a student starting with fundamental concepts, or if you are a researcher involved in complex systems, MATLAB provides you with many tools and built-in functions.

The Basics of Speech and Audio Signal Processing in MATLAB

The processing of speech and audio signals involves analysis, transformation, and manipulation of sound signals, more so human speech. MATLAB takes care of all this with its inbuilt functions and toolboxes and a friendly environment for testing and development.

1. Working with Audio Files

• To read .wav audio files, use audioread().
• To write changes to audio, use audiowrite().

Example:
[y, fs] = audioread('sample.wav');
sound(y fs);

2. Visualizing Sound Data

• To view in the time domain, try plot(y).
• To analyze in the frequency domain, employ tools such as fft() or spectrogram().
• Example for a spectrogram:
• Spectrogram(y 256, 200 256, fs, 'yaxis');

3. Performing Common Tasks

• Use filter() or filtfilt() to filter.
• Trimming audio and normalizing it are other simple tasks.
• Breaking audio signals into frames and windows helps analyze them over short periods.

4. Extracting Features

• Some features are among commonly extracted ones, such as MFCCs, pitch, energy, and zero-crossing rate.
• There exist features which can be extracted with the help of tools such as the Audio Toolbox and the Signal Processing Toolbox.

5. Reducing Noise

• Filters or spectral subtraction methods help remove background noise from signals.

6. Detecting Voice Activity (VAD)

• Identify sections where speech is present even in noisy.

7. Analyzing Pitch and Frequency

• Functions like pitch(), harmonicRatio(), and fundamentalFreq() assist in finding the pitch of speech.

Advantages of MATLAB in Audio Processing

✅ Friendly Setup
MATLAB suite of tools provides visual command-line and graphical user interface for easy prototyping.
✅ Pre-Made Functions
Time-saving functions for audio input, output, analysis, and display come ready to use.
✅ Extra Toolboxes
Special toolboxes like Audio Toolbox, DSP Toolbox, and Machine Learning Toolbox help with tricky tasks.
✅ Top-Notch Visuals
Strong plotting tools aid in checking signals and fixing bugs.
✅ Quick Testing for Study and Learning
Often used in schools and research to teach signal processing ideas.

Applications of Speech and Audio Signal Processing in MATLAB

• Speech Recognition Systems – Clean up speech, pull out key features, and build models.
• Emotion & Speaker Identification – Figure out who's talking and how they feel.
• Noise Cancellation Applications – Make sound clearer in phone systems.
• Music Signal Analysis – Look at music to find its beat, pitch, speed, and style.
• Voice-Controlled Applications – Let people talk to machines in smart homes and phone apps.
• Educational Demonstrations – Show how real-world sound systems work to help people learn.

MATLAB, with its huge set of toolboxes and built-in functions, provides a great platform to explore and put audio processing apps into action.

This blog will guide you through key parts of speech and audio signal processing using MATLAB from finding pitch and getting rid of noise, to spotting voice activity (VAD) and pulling out features. If you're new to this or have some experience, this guide will help you use theory in real-world projects.

1. Pitch Detection Using MATLAB

In our perception, its frequency content is called pitch. In musical analysis, speech recognition, and speaker classification, pitch plays an important role.

Applying MATLAB’s Built-In pitch() Tool

[y, fs] = audioread('voice_sample.wav');
f0 = pitch(y fs);
plot(f0);
title('Estimated Pitch of Audio Signal');
xlabel('Frame'); ylabel('Frequency (Hz)');

Why It Is Useful
• Helps identify who is speaking 
• Assists in tracking melodies or fine-tuning music
• It is a key feature in voice-related technology

2. Getting Rid of Noise (Denoising)

Unwanted noise can lower how well speech systems work. MATLAB provides methods to remove noise, like spectral subtraction, adaptive filtering, and wavelet-based denoising.

How to Do Basic Noise Reduction with Spectral Subtraction

This is an example function. Actual implementation or a toolbox is required.

clean_audio = spectralSubtract(y, fs);
sound(clean_audio fs);

Why It Helps:
• Speech signals become easier to understand
• Boosts accuracy in speech recognition and voice activity detection
• Helps manage audio in live scenarios

3. Detecting Voice Activity (VAD)

VAD identifies when someone is talking in an audio stream. Systems like call recording, speech coding, and telecom networks rely on this.

Energy-Based VAD Example

frameLength = round(0.02 * fs);
frames = buffer(y frameLength);
energy = sum(frames.^2);
threshold = 0.01; % Adjust this if needed
vad = energy > threshold;
plot(vad);
title('Voice Activity Detection');

Uses:

Skip parts with no speech in recordings.

Trigger systems that need voice input.

Simplify speech recognition by lowering processing.

4. Audio Feature Extraction

Identifying the right features in speech helps build smarter systems. A few used features are:

MFCCs (Mel-Frequency Cepstral Coefficients)

Zero Crossing Rate

Spectral Centroid

Chroma and Pitch Features

Example: Extracting MFCC

coeffs = mfcc(y fs);
imagesc(coeffs');
axis xy;
title('MFCC Features');
xlabel('Frame');
ylabel('Coefficient Index');

Applications:
Identifying speakers
Determining emotions
Detecting music genres
Input to train machine learning models

Hands-On Project Ideas

Take a look at some straightforward projects to apply these ideas:

1. Emotion Identification in Speech
Pull out MFCC features and pitch
Use a labeled dataset to teach a classifier emotions

2. Voice Recorder with Noise Cancellation
Capture audio that includes background noise
Remove the noise and store the cleaned version

3. Home Automation Using Voice Commands
Use VAD to pick up spoken commands
Trigger actions tied to specific recognized words

Conclusion

MATLAB offers a powerful setup to analyze and work with speech and audio signals, helping users move from simple tasks to complex projects. You can write a few lines of code to find pitch, reduce noise, figure out when speech occurs, or pull out useful features. These techniques matter a lot when creating voice-based tools for today’s smart tech world.

Begin with the basics, keep practicing, and design your smart audio systems by using what MATLAB provides.

Want to know more about Speech and Audio Signal Processing in MATLAB?

Look at our expert project assistance at TakeoffProjects – where we bridge the gap between theory and practice!