Hughes Electronics Corporation

Bangalore R Udaya Bhaskar

Gaguk Zakaria

Kumar Swaminathan

Srinivas Nandkumar

A system determines a voicing measure as a measure of the degree of signal periodicity and uses the determined voicing measure to quantize the spectral magnitude of the slowly evolving waveform (SEW) and the modeling of the SEW and rapidly evolving waveform (REW) phase spectra.

Voicing measure as an estimate of signal periodicity for a frequency domain interpolative speech codec system

Encoding of prototype waveform components applicable to telecommunication systems provides improved voice quality enabling a dual-channel mode of operation which permits more users to communicate over the same physical channel. A prototype word (PW) gain is vector quantized using a vector quantizer (VQ) that explicitly populates a codebook by representative steady state and transient vectors of PW gain for tracking the abrupt variations in speech levels during onsets and other non-stationary events, while maintaining the accuracy of the speech level during stationary conditions.

Spectral magnitude modeling and quantization in a frequency domain interpolative speech codec system

Encoding of prototype waveform components applicable to GeoMobile and Telephony Earth Station (TES) providing improved voice quality enabling a dual-channel mode of operation which permits more users to communicate over the same physical channel. A prototype word (PW) gain is vector quantized using a vector quantizer (VQ) that explicitly populates the codebook by representative steady state and transient vectors of PW gain for tracking the abrupt variations in speech levels during onsets and other non-stationary events, while maintaining the accuracy of the speech level during stationary conditions. The rapidly evolving waveform (REW) and slowly evolving waveform (SEW) component vectors are converted to magnitude-phase. The variable dimension SEW magnitude vector is quantized using a hierarchical approach, i.e., a fixed dimension SEW mean vector computed by a sub-band averaging of SEW magnitude spectrum, and only the REW magnitude is explicitly encoded. The REW magnitude vector sequence is normalized to unity RMS value, resulting in a REW magnitude shape vector and a REW gain vector. The normalized REW magnitude vectors are modeled by a multi-band sub-band model which converts the variable dimension REW magnitude shape vectors, e.g., six dimensional REW sub-band vectors. The sub-band vectors are averaged over time, resulting in a single average REW sub-band vector for each frame. At the decoder, the full-dimension REW magnitude shape vector is obtained from the REW sub-band vector by a piecewise-constant construction. The REW phase vector is regenerated at the decoder based on the received REW gain vector and the voicing measure, which determines a weighted mixture of SEW component and a random noise that is passed through a high pass filter to generate the REW component. The high pass filter poles are adjusted based on the voicing measure to control the REW component characteristics. At the output the filter, the magnitude of the REW component is scaled to match the received REW magnitude vector.

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North Potomac, MD, United States of America

Bangalore R Udaya Bhaskar