Abstract: A non-intrusive speech quality estimation technique is based on statistical or probability models such as Gaussian Mixture Models (“GMMs”). Perceptual features are extracted from the received speech signal and assessed by an artificial reference model formed using statistical models. The models characterize the statistical behavior of speech features. Consistency measures between the input speech features and the models are calculated to form indicators of speech quality. The consistency values are mapped to a speech quality score using a mapping optimized using machine learning algorithms, such as Multivariate Adaptive Regression Splines (“MARS”). The technique provides competitive or better quality estimates relative to known techniques while having lower computational complexity.

Abstract: A non-intrusive speech quality estimation technique is based on statistical or probability models such as Gaussian Mixture Models (“GMMs”). Perceptual features are extracted from the received speech signal and assessed by an artificial reference model formed using statistical models. The models characterize the statistical behavior of speech features. Consistency measures between the input speech features and the models are calculated to form indicators of speech quality. The consistency values are mapped to a speech quality score using a mapping optimized using machine learning algorithms, such as Multivariate Adaptive Regression Splines (“MARS”). The technique provides competitive or better quality estimates relative to known techniques while having lower computational complexity.

Abstract: The perceptual scalable audio coding/decoding technique lies in the use of a psychoacoustic mask to guide residue coding in enhancement layer coders. At the encoder, a psychoacoustic mask is calculated for the enhancement layer coders or is simply extracted from the coded base layer bitstream. One can also decode the coded base layer bitstream into the audio waveform, and calculate the psychoacoustic mask from the decoded base layer waveform. Furthermore, a predictive technology can be used to refine the psychoacoustic mask derived from the base layer bitstream to form a more accurate psychoacoustic mask of the enhancement layer. In addition, one can calculate the enhancement layer psychoacoustic mask from the original audio, and send the difference between the enhancement layer psychoacoustic mask and the base layer psychoacoustic mask as side information to the decoder. This psychoacoustic mask may then be used for the perceptual coding and decoding of the residue.

Abstract: The present invention relates to systems and methods for compressing, decompressing, and transmitting video data. The systems and methods include pixel by pixel motion estimation and compensation and efficient quantization of residual errors. The present invention applies block estimation of the residual error produced by motion compensation. The block estimation is applied by a local decoder to generate synthesized blocks of video data. The block estimation approximated uses a set of predetermined motion estimation errors that are stored as error vectors in a codebook. The codebook is included in an encoder of the present invention and converts an error vector for each block to an error vector index. The error vector index, which introduces minimal transmission burden, is then sent from the encoder to a target decoder. A receiving decoder also includes a copy of the codebook and converts the error vector index to its associated error vector for reconstruction of video data.

Abstract: A non-intrusive speech quality estimation technique is based on statistical or probability models such as Gaussian Mixture Models (“GMMs”). Perceptual features are extracted from the received speech signal and assessed by an artificial reference model formed using statistical models. The models characterize the statistical behavior of speech features. Consistency measures between the input speech features and the models are calculated to form indicators of speech quality. The consistency values are mapped to a speech quality score using a mapping optimized using machine learning algorithms, such as Multivariate Adaptive Regression Splines (“MARS”). The technique provides competitive or better quality estimates relative to known techniques while having lower computational complexity.

Abstract: Auditory processing is used in conjunction with cognitive mapping to produce an objective measurement of speech quality that approximates a subjective measurement such as MOS. In order to generate a data model for measuring speech quality from a clean speech signal and a degraded speech signal, the clean speech signal is subjected to auditory processing to produce a subband decomposition of the clean speech signal; the degraded speech signal is subjected to auditory processing to produce a subband decomposition of the degraded speech signal; and cognitive mapping is performed based on the clean speech signal, the subband decomposition of the clean speech signal, and the subband decomposition of the degraded speech signal. Various statistical analysis techniques, such as MARS and CART, may be employed, either alone or in combination, to perform data mining for cognitive mapping.