Design of recognize the words “Yes” and “No” played from a sound using MATLAB

Introduction

In this project, MATLABSolutions aim to conduct digital signal processing (DSP) analysis on audio files containing the words "yes" and "no," both with and without sinusoidal waves. We will start by gathering all the audio files and then create an algorithm to detect the words in the audio using spectrum analysis. By analyzing audio recordings with the words "yes" and "no," DSP can provide detailed insights into their properties and underlying frequency components.

The frequency content of these sounds can be further analyzed by adding a sinusoidal wave, allowing for a more comprehensive examination of the effects of various frequency components. These audio files will undergo DSP analysis using methods such as Fast Fourier Transform (FFT), digital filtering, and time-frequency analysis. By transforming the audio signal from the time domain to the frequency domain using FFT, we can identify and analyze individual frequency components. Digital filtering can eliminate or reduce specific frequency ranges, providing a clearer view of the remaining components. Time-frequency analysis techniques, such as spectrograms or wavelet transformations, visually represent the frequency content of the audio stream over time.

Using MATLAB, we will develop the algorithm and perform the audio analysis. By comparing the analysis of these audio files with and without sinusoidal waves, it is possible to determine the impact of these additional frequency components on the overall sound. Understanding the effects of various forms of distortion or noise on audio signals can be very beneficial. By contrasting the characteristics of the "yes" and "no" sounds, such as variations in pitch or frequency content, we can gain further insights.

The proposed technique is a fundamental example of DSP analysis on audio files using MATLAB. The project focuses on three primary DSP methods: FFT, digital filtering, and time-frequency analysis. These methods are commonly employed for audio signal analysis and can reveal information about the frequency content and properties of the signals. DSP methods are extensively used for audio and voice signal analysis, improvement, and compression. For instance, speech coding methods compress speech signals for efficient transmission across communication networks, digital filters eliminate noise from audio signals, and FFT analyzes the frequency content of audio signals.

Methodology

We are provided with a dataset of audio files containing the words "yes" and "no." Initially, we will plot the frequency response and spectrum of both words. Then, we will determine the cut-off frequencies for low-pass and high-pass filters based on the visualized frequency data. By observing the differences in magnitude, we note that the "yes" sound has a higher magnitude due to its larger high-frequency content compared to the "no" sound.

Next, we will design and apply filters at the cut-off frequencies. To remove the sinusoidal wave, we will create a filter to eliminate the 1kHz frequency and perform FFT on the signal to store the magnitude of all audio signals separately for the words "yes" and "no." Using the two signals separated by the low-pass and high-pass filter outputs, we will compute the power ratio between them to obtain information about the words "yes" and "no." We will find the threshold value for the power ratio for non-sinusoidal signal data and test the algorithm on data containing noise and sinusoidal waveforms. The predicted results with the actual class will be displayed in the command window.

The FFT technique is widely used for audio analysis in various industries, such as music, speech, and signal processing. The Fourier transform, a mathematical method that separates a time-domain signal into its frequency components, is the foundation of FFT theory.