Adaptive V/UV Speech Detection Based on Characterization of Background Noise

The paper presents an adaptive system for Voiced/Unvoiced (V/UV) speech detection in the presence of background noise. Genetic algorithms were used to select the features that offer the best V/UV detection according to the output of a background Noise Classifier (NC) and a Signal-to-Noise Ratio Estimation (SNRE) system. The system was implemented, and the tests performed using the TIMIT speech corpus and its phonetic classification. The results were compared with a nonadaptive classification system and the V/UV detectors adopted by two important speech coding standards: the V/UV detection system in the ETSI ES 202 212 v1.1.2 and the speech classification in the Selectable Mode Vocoder (SMV) algorithm. In all cases the proposed adaptive V/UV classifier outperforms the traditional solutions giving an improvement of 25% in very noisy environments

A model of collaborative innovation between local government and tourism operators

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Performance Evaluation And Comparison Of Itu-T/etsi Voice Activity Detectors

The paper proposes a performance evaluation and comparison of recent ITU-T and ETSI voice activity detection algorithms. The comparison was made using both objective and psychoacoustic parameters, so as to have reliable judgements that were close to subjective ones. A highly varied speech database was also set up to evaluate the extent to which VADs depend on language, the signal to noise ratio, or the power level

FPGA Implementation of a BPSK Modulator with Frequency Hopping

PSK modulations are very widespread in communication due to their robustness to the noise. In order to avoid interference or for anti-jamming purposes, frequency hopping may be applied. In this work we present an FPGA implementation of a BPSK modulator based on frequency hopping. The results shows good performance, more than 300 MHz of clock system, low area occupation and low power dissipation (about 100 mW)