An autocorrelation calculation unit 21 calculates an autocorrelation R.sub.o(i) from an input signal. A prediction coefficient calculation unit 23 performs linear prediction analysis by using a modified autocorrelation R′.sub.o(i) obtained by multiplying a coefficient w.sub.o(i) by the autocorrelation R.sub.o(i). It is assumed here, for each order i of some orders i at least, that the coefficient w.sub.o(i) corresponding to the order i is in a monotonically increasing relationship with an increase in a value that is negatively correlated with a fundamental frequency of the input signal of the current frame or a past frame.

A method reduces noise in an audio signal. In the method a signal component subsequent to the prediction time is predicted for a plurality of prediction times with reference to signal components of the audio signal that are respectively prior to the prediction time. A predicted audio signal is formed from the signal components respectively following a prediction time, and a noise-reduced audio signal is generated based on the predicted audio signal.

Innovations in phase quantization during speech encoding and phase reconstruction during speech decoding are described. For example, to encode a set of phase values, a speech encoder omits higher-frequency phase values and/or represents at least some of the phase values as a weighted sum of basis functions. Or, as another example, to decode a set of phase values, a speech decoder reconstructs at least some of the phase values using a weighted sum of basis functions and/or reconstructs lower-frequency phase values then uses at least some of the lower-frequency phase values to synthesize higher-frequency phase values. In many cases, the innovations improve the performance of a speech codec in low bitrate scenarios, even when encoded data is delivered over a network that suffers from insufficient bandwidth or transmission quality problems.

Innovations in phase quantization during speech encoding and phase reconstruction during speech decoding are described. For example, to encode a set of phase values, a speech encoder omits higher-frequency phase values and/or represents at least some of the phase values as a weighted sum of basis functions. Or, as another example, to decode a set of phase values, a speech decoder reconstructs at least some of the phase values using a weighted sum of basis functions and/or reconstructs lower-frequency phase values then uses at least some of the lower-frequency phase values to synthesize higher-frequency phase values. In many cases, the innovations improve the performance of a speech codec in low bitrate scenarios, even when encoded data is delivered over a network that suffers from insufficient bandwidth or transmission quality problems.

An autocorrelation calculation unit 21 calculates an autocorrelation R.sub.O(i) from an input signal. A prediction coefficient calculation unit 23 performs linear prediction analysis by using a modified autocorrelation R.sub.O(i) obtained by multiplying a coefficient w.sub.O(i) by the autocorrelation R.sub.O(i). It is assumed here, for each order i of some orders i at least, that the coefficient w.sub.O(i) corresponding to the order i is in a monotonically increasing relationship with an increase in a value that is negatively correlated with a fundamental frequency of the input signal of the current frame or a past frame.

An autocorrelation calculation unit 21 calculates an autocorrelation R.sub.O(i) from an input signal. A prediction coefficient calculation unit 23 performs linear prediction analysis by using a modified autocorrelation R.sub.O(i) obtained by multiplying a coefficient w.sub.O(i) by the autocorrelation R.sub.O(i). It is assumed here, for each order i of some orders i at least, that the coefficient w.sub.O(i) corresponding to the order i is in a monotonically increasing relationship with an increase in a value that is negatively correlated with a fundamental frequency of the input signal of the current frame or a past frame.

A system may receive a communication from a user, which may include a vocal sample. The system may transform the vocal sample from a wavelength domain into a frequency domain. The system may determine a divergence of one or more amplitude values of the transformed frequency domain from a predetermined frequency distribution. According to some embodiments, the predetermined frequency distribution may be a Benford's distribution. When the divergence exceeds a predetermined threshold, the system may execute one or more security measures. The one or more security measures may include (i) transferring the user from an automated operator to a human operator, (ii) requiring second factor authentication from the user, and/or (iii) denying a user-initiated request.

A system may receive a communication from a user, which may include a vocal sample. The system may transform the vocal sample from a wavelength domain into a frequency domain. The system may determine a divergence of one or more amplitude values of the transformed frequency domain from a predetermined frequency distribution. According to some embodiments, the predetermined frequency distribution may be a Benford's distribution. When the divergence exceeds a predetermined threshold, the system may execute one or more security measures. The one or more security measures may include (i) transferring the user from an automated operator to a human operator, (ii) requiring second factor authentication from the user, and/or (iii) denying a user-initiated request.

This invention provides a means to identify unknown media programming using the audio component of said programming. The invention extracts audio information from the media received by consumer electronic devices such as smart TVs and TV set-top boxes then conveys said information to a remote server means which will in turn identify said audio information of unknown identity by way of testing against a database of known audio segment information. The system identifies unknown media programming in real-time such that time-sensitive services may be offered such as interactive television applications providing contextually related information or television advertisement substitution. Other uses include tracking media consumption among many other services.

A signal analysis device (1) includes an estimation unit (10) that, when a parameter for modeling spatial characteristics of signals from N signal sources (where N is an integer equal to or larger than 2) is a spatial parameter, estimates a signal source position prior probability which is a mixture weight for modeling a prior distribution of the spatial parameter with respect to each signal source using a mixture distribution that is a linear combination of prior distributions of the spatial parameter with respect to K signal source position candidates (where K is an integer equal to or larger than 2), and which is a probability that a signal arrives from each signal source position candidate per signal source.