This code is a C++ program designed for vowel recognition using signal processing techniques. It processes audio signals to identify which vowel is being spoken. The program can be used for training a dataset, testing the accuracy of the recognition system, and performing real-time vowel recognition.
DC Shift and Normalization: The code adjusts the signal to remove any DC offset and normalizes the signal amplitude.
Framing: The signal is divided into frames of size 320 samples.
Hamming Window: A Hamming window is applied to each frame to reduce spectral leakage.
Autocorrelation: The autocorrelation of the signal is computed to analyze its periodicity.
Levinson-Durbin Algorithm: This algorithm is used to compute the Linear Predictive Coding (LPC) coefficients, which are essential for speech analysis.
Cepstral Coefficients: The code computes cepstral coefficients, which are used to represent the short-term power spectrum of the signal.
Training: The program trains on a dataset of vowel sounds. It processes multiple audio files for each vowel, computes the cepstral coefficients, and stores them.
Testing: The program tests the recognition system using a separate set of audio files. It computes the Tokhura distance between the test data and the trained data to identify the vowel.
Real-Time Recognition:
The program can also perform real-time vowel recognition by recording audio input, processing it, and comparing it against the trained dataset.
The program reads and writes data to files for training and testing purposes. It uses file I/O operations to store and retrieve cepstral coefficients and other data.
The implementation of the Levinson-Durbin algorithm and the computation of cepstral coefficients are evidence of advanced signal processing techniques being used.
The ability to perform real-time vowel recognition is a significant feature. It shows that the system can process audio input on-the-fly, which is essential for real-world applications.
The code includes a testing phase where the accuracy of the vowel recognition system is evaluated. The results of these tests can be used as evidence of the system's reliability and performance.
The use of helper functions and modular design demonstrates good programming practices, which can be highlighted in technical documentation.
The code can be adapted for various applications, such as voice assistants, automated transcription services, and language learning tools.