Speech Pre-emphasis and De-emphasis
Speech pre-emphasis and de-emphasis techniques for preliminary voice processing and noise reduction, with code implementation considerations.
MATLAB Program for Pitch Frequency Extraction from Speech Signals
A MATLAB implementation that extracts pitch frequency from speech signals. The program processes .wav audio files as input and outputs pitch frequency values for each frame, featuring advanced signal processing algorithms like autocorrelation or cepstrum analysis for accurate pitch detection.
Pitch Shifting without Tempo Alteration for Voice Processing
This program implements pitch shifting while maintaining original tempo, enabling male-to-female voice conversion and vice versa, with comprehensive audio manipulation capabilities.
Voice Transformation Through Frequency and Speed Modulation Techniques
1. Implement voice recording with time-frequency domain processing to achieve voice transformation through pitch shifting and time stretching, ensuring processed audio maintains intelligibility. 2. Develop GUI for visualizing input/output signals in both time and frequency domains, with comparative waveform analysis for different parameter settings.
HMM Initial Recognition Estimation
HMM initial recognition estimation with compact implementation, ideal for learning Hidden Markov Model applications in speech processing with minimal code complexity.
Speech Spectrum Analysis with MATLAB Implementation
MATLAB source codes for speech processing, featuring spectrum analysis algorithms and related audio processing techniques.
Speech Analysis and Kalman Filtering Implementation
Voice Signal Processing and Kalman Filter Algorithm with Code Integration
Blind Signal Separation: Theory and FastICA Algorithm Implementation
Blind Signal Separation (BSS) represents a cutting-edge research topic in signal processing with broad applications across wireless communications, medical diagnostics, speech processing, and seismic signal analysis. The FastICA algorithm, based on negentropy maximization, serves as a powerful method for separating mixed signals. This approach operates by processing non-Gaussian signals under independence assumptions to extract hidden source signals from observed mixtures. Key implementation aspects include whitening preprocessing, iterative optimization for independence, and convergence validation techniques.
Speech Short-Time Spectrum Analysis
MATLAB-based short-time spectral analysis of speech signals, covering spectrum analysis, spectrogram analysis, and result interpretation with implementation details
Levinson-Durbin Algorithm
The Levinson-Durbin Algorithm for Linear Predictive Control in Speech Processing and Spectral Envelope Estimation