Abstract: A method of performing distance estimation between a first recording device at a first location and a second recording device at a second location includes: estimating acoustic relative transfer function (RTF) between the first recording device and the second recording device for a sound signal, e.g., by applying an improved proportionate normalized least mean square (IPNLMS) filter; and estimating the distance between the first recording device and the second recording device based on the RTF. The at least one acoustic feature extracted from the RTF estimated between the first recording device and the second recording device includes at least one of clarity index, direct-to-reverberant ratio (DRR), and reverberation time. A distributed-gradient-boosting algorithm with regression trees is used in combination with signal-to-reverberation ratio (SRR) and the at least one acoustic feature extracted from the RTF to estimate the distance between the first recording device and the second recording device.

Abstract: A method of performing distance estimation between a first recording device at a first location and a second recording device at a second location includes: estimating acoustic relative transfer function (RTF) between the first recording device and the second recording device for a sound signal, e.g., by applying an improved proportionate normalized least mean square (IPNLMS) filter; and estimating the distance between the first recording device and the second recording device based on the RTF. The at least one acoustic feature extracted from the RTF estimated between the first recording device and the second recording device includes at least one of clarity index, direct-to-reverberant ratio (DRR), and reverberation time. A distributed-gradient-boosting algorithm with regression trees is used in combination with signal-to-reverberation ratio (SRR) and the at least one acoustic feature extracted from the RTF to estimate the distance between the first recording device and the second recording device.

Abstract: A system, method and computer program product are described for voice activity detection (VAD) within a digitally encoded bitstream. A parameter extraction module is configured to extract parameters from a sequence of coded frames from a digitally encoded bitstream containing speech. A VAD classifier is configured to operate with input of the digitally encoded bitstream to evaluate each coded frame based on bitstream coding parameter classification features to output a VAD decision indicative of whether or not speech is present in one or more of the coded frames.

Abstract: The present disclosure is directed towards a method for discontinuous transmission (“DTX”) bandwidth reduction. The method may include receiving, at a processor, a frame identified as speech and determining that the frame was mistakenly identified as speech based upon, at least in part, a voice activity detection algorithm. The method may further include labeling the frame as a silence indicator frame.

Abstract: The present disclosure is directed towards a process for estimating the signal to noise ratio of a speech signal. The process may include receiving, at a computing device, a speech signal having a bitstream and a signal-to-noise ratio (“SNR”) associated therewith. The process may further include estimating the SNR directly from the bitstream or using a partial decoder that is configured to extract one or more parameters, the parameters including at least one of a fixed codebook gain, an adaptive codebook gain, a pitch lag, and a line spectral frequency (“LSF”) coefficient.

Abstract: The present disclosure is directed towards a method for discontinuous transmission (“DTX”) bandwidth reduction. The method may include receiving, at a processor, a frame identified as speech and determining that the frame was mistakenly identified as speech based upon, at least in part, a voice activity detection algorithm. The method may further include labeling the frame as a silence indicator frame.

Abstract: The present disclosure is directed towards a process for estimating the signal to noise ratio of a speech signal. The process may include receiving, at a computing device, a speech signal having a bitstream and a signal-to-noise ratio (“SNR”) associated therewith. The process may further include estimating the SNR directly from the bitstream or using a partial decoder that is configured to extract one or more parameters, the parameters including at least one of a fixed codebook gain, an adaptive codebook gain, a pitch lag, and a line spectral frequency (“LSF”) coefficient.

Abstract: A system, method and computer program product are described for voice activity detection (VAD) within a digitally encoded bitstream. A parameter extraction module is configured to extract parameters from a sequence of coded frames from a digitally encoded bitstream containing speech. A VAD classifier is configured to operate with input of the digitally encoded bitstream to evaluate each coded frame based on bitstream coding parameter classification features to output a VAD decision indicative of whether or not speech is present in one or more of the coded frames.

Abstract: A noise-environment storing unit stores therein a compensation vector for compensating a feature vector of a speech. A feature-vector extracting unit extracts the feature vector of the speech in each of a plurality of frames. A noise-environment-series estimating unit estimates a noise-environment series based on a feature-vector series and a degree of similarity. A calculating unit obtains a compensation vector corresponding to each noise environment in estimated noise-environment series based on the compensation vector present in the noise-environment storing unit. A compensating unit compensates the extracted feature vector of the speech based on obtained compensation vector.

Abstract: A feature extracting unit extracts a feature vector of an input speech. A similarity calculating unit calculates degrees of similarity for each of a plurality of noise environments, based on the feature vector. A compensation-vector calculating unit acquires a first compensation vector from a storing unit, calculates a second compensation vector based on the first compensation vector, and calculates a third compensation vector by weighting and summing the second compensation vector with the degree of similarity as weights. A compensating unit compensates the feature vector based on the third compensation vector.

Abstract: A noise-environment storing unit stores therein a compensation vector for compensating a feature vector of a speech. A feature-vector extracting unit extracts the feature vector of the speech in each of a plurality of frames. A noise-environment-series estimating unit estimates a noise-environment series based on a feature-vector series and a degree of similarity. A calculating unit obtains a compensation vector corresponding to each noise environment in estimated noise-environment series based on the compensation vector present in the noise-environment storing unit. A compensating unit compensates the extracted feature vector of the speech based on obtained compensation vector.