Abstract: A disclosed method includes monitoring an audio signal energy level while having a noise suppressor deactivated to conserve battery power, buffering the audio signal in response to a detected increase in the audio energy level, activating and running a voice activity detector on the audio signal in response to the detected increase in the audio energy level and activating and running a noise estimator in response to voice being detected in the audio signal by the voice activity detector. The method may further include activating and running the noise suppressor only if the noise estimator determines that noise suppression is required. The method activates and runs a noise type classifier to determine the noise type based on information received from the noise estimator and selects a noise suppressor algorithm, from a group of available noise suppressor algorithms, where the selected noise suppressor algorithm is the most power consumption efficient.

Abstract: An electronic device includes a microphone that receives an audio signal that includes a spoken trigger phrase, and a processor that is electrically coupled to the microphone. The processor measures characteristics of the audio signal, and determines, based on the measured characteristics, whether the spoken trigger phrase is acceptable for trigger phrase model training. If the spoken trigger phrase is determined not to be acceptable for trigger phrase model training, the processor rejects the trigger phrase for trigger phrase model training.

Abstract: A disclosed method includes monitoring an audio signal energy level while having a noise suppressor deactivated to conserve battery power, buffering the audio signal in response to a detected increase in the audio energy level, activating and running a voice activity detector on the audio signal in response to the detected increase in the audio energy level and activating and running a noise estimator in response to voice being detected in the audio signal by the voice activity detector. The method may further include activating and running the noise suppressor only if the noise estimator determines that noise suppression is required. The method activates and runs a noise type classifier to determine the noise type based on information received from the noise estimator and selects a noise suppressor algorithm, from a group of available noise suppressor algorithms, where the selected noise suppressor algorithm is the most power consumption efficient.

Abstract: A disclosed method includes monitoring an audio signal energy level while having a plurality of signal processing components deactivated and activating at least one signal processing component in response to a detected change in the audio signal energy level. The method may include activating and running a voice activity detector on the audio signal in response to the detected change where the voice activity detector is the at least one signal processing component. The method may further include activating and running the noise suppressor only if a noise estimator determines that noise suppression is required. The method may activate and runs a noise type classifier to determine the noise type based on information received from the noise estimator and may select a noise suppressor algorithm, from a group of available noise suppressor algorithms, where the selected noise suppressor algorithm is the most power consumption efficient.

Abstract: An electronic device includes a microphone that receives an audio signal that includes a spoken trigger phrase, and a processor that is electrically coupled to the microphone. The processor measures characteristics of the audio signal, and determines, based on the measured characteristics, whether the spoken trigger phrase is acceptable for trigger phrase model training. If the spoken trigger phrase is determined not to be acceptable for trigger phrase model training, the processor rejects the trigger phrase for trigger phrase model training.

Abstract: A method performed by a portable communication device includes determining a list of at least one target device in proximity to the portable communication device. The method further includes receiving a set of non-tactile user commands that indicates selected content and a selected set of target devices on the list and controlling sharing of the selected content between the portable communication device and the selected set of target devices.

Abstract: An electronic device includes a microphone that receives an audio signal that includes a spoken trigger phrase, and a processor that is electrically coupled to the microphone. The processor measures characteristics of the audio signal, and determines, based on the measured characteristics, whether the spoken trigger phrase is acceptable for trigger phrase model training. If the spoken trigger phrase is determined not to be acceptable for trigger phrase model training, the processor rejects the trigger phrase for trigger phrase model training.

Abstract: A device receives voice inputs from a user and can perform various different tasks based on those inputs. The device is trained based on the user's voice by having the user speak a desired command. The device receives the voice input from the user and applies various different voice training parameters to generate a voice model for the user. The training parameters used by the device can change over time, so the voice input used to train the device based on the user's voice is stored by the device in a protected (e.g., encrypted) manner. When the training parameters change, the device receives the revised training parameters and applies these revised training parameters to the protected stored copy of the voice input to generate a revised voice model for the user.

Abstract: A method, a system, and a computer program product reducing noise in audio received by at least one microphone. The method includes determining, from an audio signal received by at least one primary microphone of an electronic device, whether a user that is proximate to the electronic device is currently speaking. The method further includes, in response to determining that a user is not currently speaking, receiving a first audio using a first microphone subset from among a plurality of microphones and receiving at least one second audio using at least one second microphone subset from among the plurality of microphones. The method further includes generating a composite signal from the first audio and the second audio. The method further includes collectively processing the audio signal and the composite signal to generate a modified audio signal having a reduced level of noise.

Abstract: A method for controlling echo of a voice recognition system includes receiving at least two audio input signals corresponding to sound sensed by at least two microphones in a physical space. A first audio input signal of the at least two audio input signals is received on a primary channel, and each remaining audio input signal is received through a respective secondary channel. The method includes selecting, by a processor, based on an echo power level of a speaker in the physical space, a subset of echo control functions (ECFs) from among a plurality of ECFs of a multistage echo control system. Each ECF modifies the at least two audio input signals to reduce echo. The method includes generating a corresponding number of audio output signals by processing the signals received on the primary and secondary channels through the selected subset of ECFs, and outputting the audio output signals.

Abstract: A method for controlling echo of a voice recognition system includes receiving at least two audio input signals corresponding to sound sensed by at least two microphones in a physical space. A first audio input signal of the at least two audio input signals is received on a primary channel, and each remaining audio input signal is received through a respective secondary channel. The method includes selecting, by a processor, based on an echo power level of a speaker in the physical space, a subset of echo control functions (ECFs) from among a plurality of ECFs of a multistage echo control system. Each ECF modifies the at least two audio input signals to reduce echo. The method includes generating a corresponding number of audio output signals by processing the signals received on the primary and secondary channels through the selected subset of ECFs, and outputting the audio output signals.

Abstract: A method includes obtaining, by a processor, an audio echo signal and an audio desired signal from an acoustic echo correction stage of an electronic device, and converting the echo signal and the desired signal to the frequency domain. The method further includes grouping, by the processor, frequency bin results of respective frequency domain converted echo and desired signals into respective echo and desired sub-bands. A sub-band suppressor gain is estimated based on an estimated sub-band energy for the echo and desired sub-bands. The method further includes modulating the frequency domain converted desired signal to compensate for residual echo, the modulating based, at least in part, on the estimated sub-band suppressor gain, and the modulating producing a compensated frequency domain converted echo signal. The method also includes converting the compensated frequency domain converted desired signal into time domain converted audio output signal.

Abstract: A method performed by a portable communication device includes determining a list of at least one target device in proximity to the portable communication device. The method further includes receiving a set of non-tactile user commands that indicates selected content and a selected set of target devices on the list and controlling sharing of the selected content between the portable communication device and the selected set of target devices.

Abstract: An electronic device includes a microphone that receives an audio signal that includes a spoken trigger phrase, and a processor that is electrically coupled to the microphone. The processor measures characteristics of the audio signal, and determines, based on the measured characteristics, whether the spoken trigger phrase is acceptable for trigger phrase model training. If the spoken trigger phrase is determined not to be acceptable for trigger phrase model training, the processor rejects the trigger phrase for trigger phrase model training.

Abstract: A method includes obtaining, by a processor, an audio echo signal and an audio desired signal from an acoustic echo correction stage of an electronic device, and converting the echo signal and the desired signal to the frequency domain. The method further includes grouping, by the processor, frequency bin results of respective frequency domain converted echo and desired signals into respective echo and desired sub-bands. A sub-band suppressor gain is estimated based on an estimated sub-band energy for the echo and desired sub-bands. The method further includes modulating the frequency domain converted desired signal to compensate for residual echo, the modulating based, at least in part, on the estimated sub-band suppressor gain, and the modulating producing a compensated frequency domain converted echo signal. The method also includes converting the compensated frequency domain converted desired signal into time domain converted audio output signal.

Abstract: A portable device performs echo cancellation and echo suppression. An audio echo signal and an audio desired signal are obtained from an acoustic echo correction stage of the portable device. The echo and desired signals are converted to the frequency domain. Frequency bin results of the respective frequency domain converted echo and desired signals are grouped into echo and desired sub-bands. A sub-band suppressor gain is estimated based on the estimated sub-band energy for the echo and desired sub-bands. The frequency domain converted echo signal is modulated based at least in part on the estimated sub-band suppressor gain to compensate for residual echo. The compensated frequency domain converted echo signal is time domain converted into an audio output signal. The audio output signal is processed by a selected one of a voice recognition engine and a communication module transmitter.

Abstract: An electronic device includes a microphone that receives an audio signal that includes a spoken trigger phrase, and a processor that is electrically coupled to the microphone. The processor measures characteristics of the audio signal, and determines, based on the measured characteristics, whether the spoken trigger phrase is acceptable for trigger phrase model training. If the spoken trigger phrase is determined not to be acceptable for trigger phrase model training, the processor rejects the trigger phrase for trigger phrase model training.

Abstract: An electronic device includes a microphone that receives an audio signal that includes a spoken trigger phrase, and a processor that is electrically coupled to the microphone. The processor measures characteristics of the audio signal, and determines, based on the measured characteristics, whether the spoken trigger phrase is acceptable for trigger phrase model training. If the spoken trigger phrase is determined not to be acceptable for trigger phrase model training, the processor rejects the trigger phrase for trigger phrase model training.

Abstract: An electronic device includes a microphone that receives an audio signal that includes a spoken trigger phrase, and a processor that is electrically coupled to the microphone. The processor measures characteristics of the audio signal, and determines, based on the measured characteristics, whether the spoken trigger phrase is acceptable for trigger phrase model training. If the spoken trigger phrase is determined not to be acceptable for trigger phrase model training, the processor rejects the trigger phrase for trigger phrase model training.

Abstract: An electronic device includes a microphone that receives an audio signal that includes a spoken trigger phrase, and a processor that is electrically coupled to the microphone. The processor measures characteristics of the audio signal, and determines, based on the measured characteristics, whether the spoken trigger phrase is acceptable for trigger phrase model training. If the spoken trigger phrase is determined not to be acceptable for trigger phrase model training, the processor rejects the trigger phrase for trigger phrase model training.