Short-Time Analysis of Speech Signals

Speech signals are time-varying in nature, with individual parameter variations occurring more gradually than the signal itself. Consequently, measuring these parameters requires a significantly lower sampling frequency compared to the signal's original sampling rate. Through window function weighting, the signal is segmented in the time domain into local signal sequences for measurement. Proper short-time analysis requires defining two key dimensions: window length (duration of the weighted signal segment) and measurement interval (frame rate, representing the spacing between consecutive windows). Core short-time analysis operations include short-time energy (reflecting amplitude variations), short-time autocorrelation function (detecting periodicity), and short-time zero-crossing rate.

Bluetooth Frequency Hopping Simulation System

Signal sequence generation and mapping onto frequency hopping channels. The system employs a Bernoulli random signal generator module to create random signals with a frame sampling rate of 10 and sampling time of 1.5e-6. Signals are preprocessed and mapped onto a 1600 hops/second frequency hopping pattern. The modulation scheme uses FH-CPM (Frequency Hopping-Continuous Phase Modulation), where the original signal undergoes CPM modulation to produce binary symbol sequences with a pulse length of 1. Simultaneously, the frequency hopping signal at 1600 hops/s is modulated using M-FSK, generating a hopping sequence ranging from -39MHz to 39MHz. These two sequences are multiplied to produce the output signal for transmission. The receiver utilizes an identical random frequency hopping sequence for demodulation, reversing the preprocessing steps through dedicated subsystems.