Qnx Software Systems Limited

2236008 Ontario Limited

Qnx Software Systems Co.

Qnx Software Systems (wavemakers), Inc.

8758271 Canada Inc.

Blackberry Corporation

Xueman Li

Phillip Hetherington

Phillip Alan Hetherington

Pierre Zakarauskas

Shreyas Anand Paranjpe

Rajeev Nongpiur

Phil Hetherington

Frank Linseisen

Kyle Tegan MacDonald

Mark Robert Every

Alex Escott

Darrin Kenneth John Fry

David Giesbrecht

Phil A Hetherington

Philip Alan Hetherington

Philip A Hetherington

Phillip A Hetherington

Voice control in a multi-talker and multimedia environment is disclosed. In one aspect, there is provided a method comprising: receiving a microphone signal for each zone in a plurality of zones of an acoustic environment; generating a processed microphone signal for each zone in the plurality of zones of the acoustic environment, the generating including removing echo caused by audio transducers in the acoustic environment from each of the microphone signals, and removing interference from each of the microphone signals; and performing speech recognition on the processed microphone signals.

Voice control in a multi-talker and multimedia environment

The invention includes a method, apparatus, and computer program to selectively suppress wind noise while preserving narrow-band signals in acoustic data. Sound from one or several microphones is digitized into binary data. A time-frequency transform is applied to the data to produce a series of spectra. The spectra are analyzed to detect the presence of wind noise and narrow band signals. Wind noise is selectively suppressed while preserving the narrow band signals. The narrow band signal is interpolated through the times and frequencies when it is masked by the wind noise. A time series is then synthesized from the signal spectral estimate that can be listened to. This invention overcomes prior art limitations that require more than one microphone and an independent measurement of wind speed. Its application results in good-quality speech from data severely degraded by wind noise.

Method and apparatus for suppressing wind noise

An adaptive equalization system that adjusts the spectral shape of a speech signal based on an intelligibility measurement of the speech signal may improve the intelligibility of the output speech signal. Such an adaptive equalization system may include a speech intelligibility measurement module, a spectral shape adjustment module, and an adaptive equalization module. The speech intelligibility measurement module is configured to calculate a speech intelligibility measurement of a speech signal. The spectral shape adjustment module is configured to generate a weighted long-term speech curve based on a first predetermined long-term average speech curve, a second predetermined long-term average speech curve, and the speech intelligibility measurement. The adaptive equalization module is configured to adapt equalization coefficients for the speech signal based on the weighted long-term speech curve.

Adaptive equalization system

A system and method for dynamic residual noise shaping configured to reduce hiss noise in an audio signal. The system and method may detect an amount and type of hiss noise. The system and method may limit calculated noise suppression gains responsive to the detected amount and type of hiss noise. The limited noise suppression gains may be applied to the audio signal and may reduce the hiss noise.

System and method for dynamic residual noise shaping

A speech enhancement system improves speech conversion within an encoder and decoder. The system includes a first device that converts sound waves into operational signals. A second device selects a template that represents an expected signal model. The selected template models speech characteristics of the operational signals through a speech codebook that is further accessed in a communication channel.

Speech enhancement system and method

A speech enhancement system enhances transitions between speech and non-speech segments. The system includes a background noise estimator that approximates the magnitude of a background noise of an input signal that includes a speech and a non-speech segment. A slave processor is programmed to perform the specialized task of modifying a spectral tilt of the input signal to match a plurality of expected spectral shapes selected by a Codec.

Speech enhancement with minimum gating

A system improves speech intelligibility by reconstructing speech segments. The system includes a low-frequency reconstruction controller programmed to select a predetermined portion of a time domain signal. The low-frequency reconstruction controller substantially blocks signals above and below the selected predetermined portion. A harmonic generator generates low-frequency harmonics in the time domain that lie within a frequency range controlled by a background noise modeler. A gain controller adjusts the low-frequency harmonics to substantially match the signal strength to the time domain original input signal.

Speech enhancement through partial speech reconstruction

A noise injection system adds comfort noise to an audio signal. The system includes a background noise estimator that determines a spectral content of a background noise associated with the audio signal. A comfort noise generator generates a comfort noise signal having a random phase. A gain circuit adjusts the comfort noise signal based on the spectral content of the background noise. A combining circuit combines a gain-adjusted comfort noise signal and the audio signal to generate an output signal.

System for comfort noise injection

The system provides a technique for suppressing or eliminating tonal noise in and input signal. The system operates on the input signal at a plurality of frequency bins and uses information generated at a prior bin to assist in calculating values at subsequent bins. The system first identifies peaks in a signal and then determines if the peaks are from tonal effects. This can be done by comparing the estimated background noise of a current bin to the smoothed background noise of the same bin. The smoothed background noise can be calculated using an asymmetric IIR filter. When the ratio of the current background noise estimate to the currently calculated smoothed background noise is far greater than 1, tonal noise is assumed. When tonal noise is found, a number of suppression techniques can be applied to reduce the tonal noise, including gain suppression with fixed floor factor, an adaptive floor factor gain suppression technique, and a random phase technique.

Noise reduction with integrated tonal noise reduction

The present system proposes a technique called the spectro-temporal varying technique, to compute the suppression gain. This method is motivated by the perceptual properties of human auditory system; specifically, that the human ear has higher frequency resolution in the lower frequencies band and less frequency resolution in the higher frequencies, and also that the important speech information in the high frequencies are consonants which usually have random noise spectral shape. A second property of the human auditory system is that the human ear has lower temporal resolution in the lower frequencies and higher temporal resolution in the higher frequencies. Based on that, the system uses a spectro-temporal varying method which introduces the concept of frequency-smoothing by modifying the estimation of the a posteriori SNR. In addition, the system also makes the a priori SNR time-smoothing factor depend on frequency. As a result, the present method has better performance in reducing the amount of musical noise and preserves the naturalness of speech especially in very noisy conditions than do conventional methods.

Spectro-temporal varying approach for speech enhancement

A speech enhancement system improves the speech quality and intelligibility of a speech signal. The system includes a time-to-frequency converter that converts segments of a speech signal into frequency bands. A signal detector measures the signal power of the frequency bands of each speech segment. A background noise estimator measures a background noise detected in the speech signal. A dynamic noise reduction controller dynamically models the background noise in the speech signal. The speech enhancement renders a speech signal perceptually pleasing to a listener by dynamically attenuating a portion of the noise that occurs in a portion of the spectrum of the speech signal.

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Burnaby, Canada

Xueman Li