High-Quality Speech Processing Toolbox
Comprehensive speech processing toolbox featuring essential modules including frame segmentation, signal preprocessing, and fundamental frequency (F0) estimation algorithms
Fingerprint Recognition Source Code with Comprehensive Image Processing Pipeline
Complete fingerprint recognition source code featuring comprehensive image preprocessing workflow including smoothing, noise removal, and thinning/skeletonization algorithms. This implementation provides detailed guidance for beginners to learn fingerprint recognition technology through practical code examples.
MATLAB Implementation of Speech Recognition with Complete Source Code
MATLAB source code for speech recognition system including preprocessing, feature extraction, and training/recognition modules with algorithm implementations
Motion Vehicle Detection Using Background Subtraction Method
Implementation of background subtraction for detecting moving vehicles, including image preprocessing and edge extraction techniques for vehicle images
Isolated Word Speech Recognition System Based on Dynamic Time Warping (DTW)
Implementation framework including speech database construction, audio preprocessing, frame segmentation, endpoint detection, and feature analysis with code-level algorithm explanations
MATLAB Speech Recognition Algorithm with HMM Implementation
MATLAB speech recognition algorithm implementation featuring preprocessing, feature extraction, training, and recognition phases using Hidden Markov Models (HMM)
Applications in Vibration Signal Processing
MATLAB applications in vibration signal processing, featuring books and source code projects for preprocessing engineering vibration signals and modal parameter identification programs
Pattern Recognition and Image Normalization: Preprocessing in Computer Vision
Pattern Recognition and Image Normalization: Computer Vision Preprocessing Techniques with Implementation Insights
Speech Recognition for Digits 0-9 Based on MATLAB
This project implements speech recognition for passwords composed of digits 0-9 by calculating the cross-correlation function between two signals and their corresponding variance, using maximum variance as the discrimination threshold. Signal preprocessing is essential and includes FIR filter-based pre-emphasis and endpoint detection using short-term average energy and zero-crossing rate methods to extract useful signals. These preprocessing steps maximize recognition accuracy. The cross-correlation function, which quantifies signal similarity, helps differentiate test signals from template signals. In MATLAB implementation, key functions like xcorr() for cross-correlation calculation and var() for variance computation are utilized, while preprocessing involves designing FIR filters with fir1() and implementing energy/zcr-based endpoint detection algorithms.
Preprocessing and Feature Descriptors for Facial Micro-Expression Recognition
Facial micro-expressions reveal crucial insights into human emotions, even when individuals attempt to conceal their feelings. Historically, limited research has been conducted on detecting and recognizing micro-expressions using computer vision techniques. This implementation processes spontaneous micro-expression databases through preprocessing and Haar feature-based image cropping, followed by feature extraction using Local Binary Patterns on Three Orthogonal Planes (LBP-TOP) and Local Gray-Coding Patterns on Three Orthogonal Planes (LGCP-TOP) descriptors. The system employs Support Vector Machines (SVM) for detection and classification, achieving accuracy comparable to existing state-of-the-art methods.