Browsing by Author "El-Hennawey, Mohamed"

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Method and apparatus for fast DTMF detection
(Metrosol Inc; RPX Clearinghouse LLC, 2008) Fouret, Joachim; Ahmadi, Masoud; El-Hennawey, Mohamed; Liao, Can
A fast DTMF detector for filtering a packetized linear voice signal to identify whether the voice signal contains a DTMF tone. The detector includes a plurality of parallel notch filters for knocking down DTMF tones. The energies of the filtered signals output by the notch filters are calculated and criteria based upon the calculated energies are applied to determine if the voice signal contains a DTMF tone. The criteria include an energy differential test, a twist test, a low frequency tolerance test, a high frequency tolerance test, a packetized linear voice signal energy level test, a dial tone filtered signal energy level test, and a 1004 Hz filtered signal energy level test.
Method and apparatus for non-intrusive single-ended voice quality assessment in VoIP
(RPX Clearinghouse LLC, 2012) El-Hennawey, Mohamed; Goubran, Rafik; M. Radwan, Ayman; Ding, Lijing
An apparatus (1240), method, and computer program to assess VoIP speech quality (130) using access to degraded signals is provided. Different types of impairment (110) have different effect, on speech quality. Preferred embodiments address up to four different types of impairment that affect VoIP signal quality: packet loss (230), speech clipping in time (850), noise (1400) and echo. An overall assessment algorithm factors in degradation due to various impairment factors to generate an overall speech quality assessment score or value.
Method and system for speech processing for enhancement and detection
(RPX Clearinghouse LLC, 2008) Gazor, Saeed; El-Hennawey, Mohamed
A method for discriminating noise from signal in a noise-contaminated signal involves decomposing a frame of samples of the signal into decorrelated components, and using a difference between probability distributions of the noise contributions and the signal contributions to identify signal and noise. A Gaussian distribution is used to determine whether the components are only noise whereas a Laplacian distribution is used to determine whether the components contain the signal. Such discrimination may be used in speech enhancement or voice activity detection apparatus.
Single-sided speech quality measurement
(Avaya Inc, 2007) Chan, Wai-Yip; Falk, Tiago; El-Hennawey, Mohamed
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.
Speech quality measurement based on classification estimation
(RPX Clearinghouse LLC, 2006) Chan, Wai-Yip; Zha, Wei; El-Hennawey, Mohamed
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. From the large number of features extracted from the distortion surface, MARS is employed to find a smaller subset of features to form the speech quality estimator. The subset of feature variables, together with the particular manner of combining them, are jointly optimized to produce a statistically consistent estimate (data model) of subjective opinion scores such as MOS.