Abstract: To overcome the problem of poor representation of the background noise, the present invention includes a noise parameter generator (40) which uses a weighted average of auto-correlation values of the input signal generated during the noise-analysis phase. The weighting function gives less weight to the auto-correlations during the first few frames (as they may contain speech) and more weight to frames towards the end of this phase. Also included, to overcome the bursty nature of comfort noise, is a comfort noise generator (50) which gradually changes the nature of the signal from speech to pseudo-random noise after the speech-burst. The comfort noise generator (50) of the present invention excites the auto-regressive filter corresponding to the noise model with a weighted combination of the past excitation and pseudo-random noise.

Abstract: To overcome the problem of poor representation of the background noise, the present invention includes a noise parameter generator (40) which uses a weighted average of auto-correlation values of the input signal generated during the noise-analysis phase. The weighting function gives less weight to the auto-correlations during the first few frames (as they may contain speech) and more weight to frames towards the end of this phase. Also included, to overcome the bursty nature of comfort noise, is a comfort noise generator (50) which gradually changes the nature of the signal from speech to pseudo-random noise after the speech-burst The comfort noise generator (50) of the present invention excites the auto-regressive filter corresponding to the noise model with a weighted combination of the past excitation and pseudo-random noise.

Abstract: A method of using a computer (11) to perform a multi-stage vector quantization process (13a). At each stage of the process (13a) subsequent to the first stage, input vectors from the previous stage are used to search a codebook (13b) for code-vectors that minimize distortion. (FIG. 2) The search is structured so that each stage is performed with an outer loop that calculates components of distortion that do not depend on the input vector value. An inner loop, which does depend on input vector values, is used to calculate distortion values and to maintain a list of the current best output code-vectors. (FIG. 3). The first stage is a special case, having only one input vector, but is otherwise performed like the subsequent stages.

Abstract: An improved error detection and error concealment for Viterbi decoding of convolutionally encoded data is provided. The most sensitive part of the data is parity encoded and sent with parity and this data with the next most sensitive data are convolutionally encoded and sent with the least sensitive data over a transmission channel to a receiver. At the receiver the convolutionally encoded data is decoded using the Viterbi algorithm. The decoder compares the parity computed from decoded data with the decoded parity and if they are not equal generates a Bad Frame Indicator (BFI) flag and also determines which decoded parameters are likely bad and hence generates a Bad Parameter Indicator (BPI) flag for those parameters, by determining the confidence levels for the parameters and comparing against pre-selected thresholds. The decision to discard a decoded parameter is dependent on the BFI flag and the BPI flag of that parameter.

Abstract: To overcome the problem of poor representation of the background noise, the present invention includes a noise parameter generator (40) which uses a weighted average of auto-correlation values of the input signal generated during the noise-analysis phase. The weighting function gives less weight to the auto-correlations during the first few frames (as they may contain speech) and more weight to frames towards the end of this phase. Also included, to overcome the bursty nature of comfort noise, is a comfort noise generator (50) which gradually changes the nature of the signal from speech to pseudo-random noise after the speech-burst. The comfort noise generator (50) of the present invention excites the auto-regressive filter corresponding to the noise model with a weighted combination of the past excitation and pseudo-random noise.