Introduction to Speaker Recognition Technology

Oct 13, 2025 by talynt.org 90 views

Introduction to Speaker Recognition Technology

Speaker recognition is a technology that allows computers to recognize one's identity based on their voices. This technology has great potential in various applications such as security, access control, and virtual assistant. The ability to recognize and identify individuals through their voices has numerous benefits, including enhanced security, improved customer service, and increased efficiency in various industries.

Designing a System for Introduction to Speaker Recognition

In this final project, it is designed a system of introduction that utilizes anti-aliasing filters and analog to digital converters (ADC). This system uses ChebyShev's low pass filter as an anti-aliasing filter to eliminate unwanted high frequency signals and configure ADCs available on Arduino to convert analog signals into digital data. The system is designed to capture and process sound signals, eliminating noise and distortion to improve the accuracy of sound recognition.

Mechanism of System Action

The system begins with the sound recording process using a microphone. The acoustic waves captured by the microphone are converted into analog signals. This analog signal then passes through ChebyShev's low pass filter to remove high frequency signals that can cause distortion of the resulting digital signal. After passing the filter, the analog signal is converted into digital data using ADC installed on the Arduino microcontroller.

The digital data obtained is then sent to a computer through USB to serial communication. On a computer, this digital data will be processed using MATLAB software for analysis and sound recognition. The system's ability to capture and process sound signals in real-time makes it an ideal solution for various applications that require user identification and verification through sound.

Data Analysis

The initial test was carried out by recording the votes from the resource person. The analysis results show that the reference sound signal has the highest amplitude value between -0.3 mV to 0.3 MV, with the amount of data received around 6623 samples of data from a total of 16,000 data within 2 seconds. The test data on the first resource person has a wider range of amplitude, which is -0.4 mV to 0.5 mV, with the amount of data received 6887 sample data.

The difference in the amount of data received between the reference data and the test shows the existence of noise that participates in the testing sound signal. The difference of 264 Sample Data can be interpreted as data noise that needs to be eliminated to increase the accuracy of sound recognition.

The Importance of Anti-Aliasasi Filter and ADC

Anti-aliasing filter plays an important role in the recognition system. This filter limits the frequency of analog signals that pass through ADC, so that only signals in the desired frequency range are converted into digital data. This prevents the emergence of aliasing, namely the signal distortion that occurs due to analog signal sampling that is too slow.

ADC is a vital component in the conversion of analog to digital signals. ADC performance in terms of resolution and sampling speed affects the quality of digital data produced. ADC resolution shows the measurement accuracy, while the sampling speed determines the amount of data collected within a certain time.

Future System Development

The introduction system of the packet designed in this final project is the first step in developing a more complex system. To increase sound recognition accuracy, some things that need to be done are:

Improved Anti-Aliasasi Filter

Adjusting anti-aliasing filters to be more effective in eliminating noise and distortion.
Using a more advanced filter design to improve the system's ability to capture and process sound signals.

Use of ADC with a Higher Resolution

Selecting ADC with a higher resolution to increase the accuracy of analog signal measurements.
Using a higher sampling speed to collect more data within a certain time.

Development of Voice Recognition Algorithms

Increasing the sound recognition algorithm used in Matlab to minimize introduction errors.
Using more advanced machine learning techniques to improve the system's ability to recognize and identify individuals through their voices.

Wider Testing

Conducting system testing using a more diverse sound dataset and more data.
Testing the system in various environments and conditions to ensure its reliability and accuracy.

The introduction system for the pentor has great potential to be developed further. With an increase in algorithms, better data processing, and increasing hardware capabilities, this system can be applied to various applications that require user identification and verification through sound.

Conclusion

The design of anti-aliasing filter and ADC for the introduction system is a crucial step in developing a more complex system for speaker recognition. The system's ability to capture and process sound signals in real-time makes it an ideal solution for various applications that require user identification and verification through sound. With further development and improvement, this system can be applied to various industries and applications, providing numerous benefits and advantages.

Future Work

Future work on this project includes:

Improving the anti-aliasing filter design to be more effective in eliminating noise and distortion.
Increasing the ADC resolution and sampling speed to improve the system's ability to capture and process sound signals.
Developing more advanced voice recognition algorithms to improve the system's ability to recognize and identify individuals through their voices.
Conducting wider testing of the system using a more diverse sound dataset and more data.

By addressing these areas, the introduction system for the pentor can be further developed and improved, providing a more accurate and reliable solution for speaker recognition and user identification.

Frequently Asked Questions

In this Q&A article, we will address some of the most common questions related to the design of anti-aliasing filter and ADC for the introduction system.

Q: What is the purpose of anti-aliasing filter in the introduction system?

A: The anti-aliasing filter is used to eliminate unwanted high frequency signals that can cause distortion of the resulting digital signal. It limits the frequency of analog signals that pass through ADC, so that only signals in the desired frequency range are converted into digital data.

Q: What is the role of ADC in the introduction system?

A: ADC is a vital component in the conversion of analog to digital signals. ADC performance in terms of resolution and sampling speed affects the quality of digital data produced. ADC resolution shows the measurement accuracy, while the sampling speed determines the amount of data collected within a certain time.

Q: How does the introduction system work?

A: The system begins with the sound recording process using a microphone. The acoustic waves captured by the microphone are converted into analog signals. This analog signal then passes through ChebyShev's low pass filter to remove high frequency signals that can cause distortion of the resulting digital signal. After passing the filter, the analog signal is converted into digital data using ADC installed on the Arduino microcontroller.

Q: What is the difference between the reference sound signal and the test sound signal?

A: The reference sound signal has the highest amplitude value between -0.3 mV to 0.3 MV, with the amount of data received around 6623 samples of data from a total of 16,000 data within 2 seconds. The test data on the first resource person has a wider range of amplitude, which is -0.4 mV to 0.5 mV, with the amount of data received 6887 sample data.

Q: What is the significance of the difference in the amount of data received between the reference data and the test?

A: The difference in the amount of data received between the reference data and the test shows the existence of noise that participates in the testing sound signal. The difference of 264 Sample Data can be interpreted as data noise that needs to be eliminated to increase the accuracy of sound recognition.

Q: How can the accuracy of sound recognition be improved?

A: To improve the accuracy of sound recognition, some things that need to be done are:

Improving the anti-aliasing filter design to be more effective in eliminating noise and distortion.
Increasing the ADC resolution and sampling speed to improve the system's ability to capture and process sound signals.
Developing more advanced voice recognition algorithms to improve the system's ability to recognize and identify individuals through their voices.
Conducting wider testing of the system using a more diverse sound dataset and more data.

Q: What are the potential applications of the introduction system?

A: The introduction system has great potential to be applied to various applications that require user identification and verification through sound, such as security, access control, and virtual assistant.

Q: What are the future directions of the introduction system?

A: The future directions of the introduction system include:

Improving the anti-aliasing filter design to be more effective in eliminating noise and distortion.
Increasing the ADC resolution and sampling speed to improve the system's ability to capture and process sound signals.
Developing more advanced voice recognition algorithms to improve the system's ability to recognize and identify individuals through their voices.
Conducting wider testing of the system using a more diverse sound dataset and more data.

By addressing these areas, the introduction system can be further developed and improved, providing a more accurate and reliable solution for speaker recognition and user identification.