Abstract: A process for preparing metal oxide comprising (i) at least one metal chosen from nickel and cobalt and optionally (ii) at least one metal chosen from manganese, lithium and aluminum.

Abstract: Embodiments may find applications to ambient noise attenuation in cell phones, for example, where a second microphone is placed at a distance from the voice microphone so that ambient noise is present at both the voice microphone and the second microphone, but where the user's voice is primarily picked up at the voice microphone. Frequency domain filtering is employed on the voice signal, so that those frequency components representing mainly ambient noise are de-emphasized relative to the other frequency components. Other embodiments are described and claimed.

Abstract: Systems and methods for reducing false alarms in keyword detection are provided. An example method includes detecting a keyword in an acoustic signal. The acoustic signal can represent at least one captured sound. The method also includes acquiring an estimate of speech activity for a portion of the acoustic signal preceding the keyword. In some embodiments, the estimate includes an average of a voice activity detection output over frames of the acoustic signal within the portion preceding the keyword. If the estimate is less than a threshold, the method can accept the keyword detection. If the estimate is larger than the threshold, the method proceeds to reject the keyword detection.

Abstract: Systems and methods for locating the end of a keyword in voice sensing are provided. An example method includes receiving an acoustic signal that includes a keyword portion immediately followed by a query portion. The acoustic signal represents at least one captured sound. The method further includes determining the end of the keyword portion. The method further includes, separating, using the end of the keyword portion, the query portion from the keyword portion of the acoustic signal. The method further includes providing the query portion, absent any part of the keyword portion, to an automatic speech recognition (ASR) system.

Abstract: Systems and methods for keyword detection using keyword repetitions are provided. An example method includes receiving an acoustic signal representing at least one captured sound. Using a keyword model, a first confidence score for the first acoustic signal may be acquired. The method also includes determining the first confidence score is less than a detection threshold within a first value. In response, lowering the threshold by a second value for a pre-determined time interval. The method also includes receiving a second acoustic signal captured during the pre-determined time interval and acquiring a second confidence score for the second acoustic signal. The method also includes determining the second confidence score equals or exceeds the lowered threshold, and then confirming keyword detection. The threshold may be restored after the pre-determined time interval. The keyword model may be temporarily replaced by a tuned keyword model to facilitate keyword detection in low SNR conditions.

Abstract: A digital signal is processed by splitting it into at least two frequency subbands and the two subband signals are downsampled. A filter is applied in at least one of the subband signals. At least one of the phase and magnitude of the subband filtered signals is matched in the transition frequency band between the two subbands.

Abstract: Described are noise suppression techniques applicable to various systems including automatic speech processing systems in digital audio pre-processing. The noise suppression techniques utilize a machine-learning framework trained on cues pertaining to reference clean and noisy speech signals, and a corresponding synthetic noisy speech signal combining the clean and noisy speech signals. The machine-learning technique is further used to process audio signals in real time by extracting and analyzing cues pertaining to noisy speech to dynamically generate an appropriate gain mask, which may eliminate the noise components from the input audio signal. The audio signal pre-processed in such a manner may be applied to an automatic speech processing engine for corresponding interpretation or processing.

Abstract: Methods for voice sensing and keyword analysis are provided. An example method allows for causing a mobile device to transition to a second power mode, from a first power mode, in response to a first acoustic signal. The method includes authenticating a user based at least in part on a second acoustic signal. While authenticating the user, the second acoustic signal is compared to a spoken keyword. The spoken keyword is analyzed for authentication strength based on the length of the spoken keyword, quality of a series of phonemes used to represent the spoken keyword, and likelihood of the series of phonemes to be detected by a voice sensing. While receiving the first and second acoustic signals, a signal to noise ratio (SNR) is determined. The SNR is used to adjust sensitivity of a detection threshold of a voice sensing.

Abstract: A digital signal is processed by splitting it into at least two frequency subbands and the two subband signals are downsampled. A filter is applied in at least one of the subband signals. At least one of the phase and magnitude of the subband filtered signals is matched in the transition frequency band between the two subbands.

Abstract: Embodiments may find applications to ambient noise attenuation in cell phones, for example, where a second microphone is placed at a distance from the voice microphone so that ambient noise is present at both the voice microphone and the second microphone, but where the user's voice is primarily picked up at the voice microphone. Frequency domain filtering is employed on the voice signal, so that those frequency components representing mainly ambient noise are de-emphasized relative to the other frequency components. Other embodiments are described and claimed.

Abstract: Noise suppression information is used to optimize or improve automatic speech recognition performed for a signal. Noise suppression can be performed on a noisy speech signal using a gain value. The gain to apply to the noisy speech signal is selected to optimize speech recognition analysis of the resulting signal. The gain may be selected based on one or more features for a current sub band and time frame, as well as one or more features for other sub bands and/or time frames. Noise suppression information can be provided to a speech recognition module to improve the robustness of the speech recognition analysis. Noise suppression information can also be used to encode and identify speech.

Abstract: A digital signal is processed by splitting it into at least two frequency subbands and the two subband signals are downsampled. A filter is applied in at least one of the subband signals. At least one of the phase and magnitude of the subband filtered signals is matched in the transition frequency band between the two subbands.

Abstract: The present technology provides a robust noise suppression system that may concurrently reduce noise and echo components in an acoustic signal while limiting the level of speech distortion. A time-domain acoustic signal may be received and be transformed to frequency-domain sub-band signals. Features, such as pitch, may be identified and tracked within the sub-band signals. Initial speech and noise models may be then be estimated at least in part from a probability analysis based on the tracked pitch sources. Speech and noise models may be resolved from the initial speech and noise models and noise reduction may be performed on the sub-band signals. An acoustic signal may be reconstructed from the noise-reduced sub-band signals.

Abstract: The present technology provides techniques for transform domain reconstruction of noise-corrupted portions of an acoustic signal to emulate speech which is obscured by the noise. Replacement transform values for the noise-corrupted portions are determined utilizing the portions of the acoustic signal which contain speech.

Abstract: Systems and methods described herein provide for low latency active noise cancellation which alleviate the problems associated with analog filter circuitry. The present technology utilizes low latency digital signal processing techniques which overcome the high latency conventionally associated with conversion between the analog and digital domains. As a result, low latency active noise cancellation is performed utilizing digital filter circuitry which is not subject to the inaccuracies and drift of analog filter components. In doing so, the present technology provides robust, high quality active noise cancellation.

Abstract: Systems and methods for voice-controlled communication connections are provided. An example system includes a mobile device being operated consecutively in listen, wakeup, authentication, and connect modes. Each of subsequent modes consumes more power than a preceding mode. The listen mode consumes less than 5 mW. In the listen mode, the mobile device listens for an acoustic signal, determines whether the acoustic signal includes voice, and upon the determination, selectively enters the wakeup mode. In the wakeup mode, the mobile device determines whether the acoustic signal includes a spoken word and, upon the determination, enters the authentication mode. In authentication mode, the mobile device identifies a user using the spoken command and, upon the identification, enters the connect mode. In the connect mode, the mobile device receives an acoustic signal, determines whether the acoustic signal includes a spoken command and performs one or more operations associated with the spoken command.

Abstract: A system (200) for processing a sound signal (212) that allows dynamic customization of perceived spatial positions and sound qualities of sound components associated with the sound signal (212). The system provides apparatus for processing a sound signal (212) that includes an input to receive the sound signal (212), a sound unmixer (204) coupled to the input to receive the sound signal (212) and unmix at least one sound stream (216) from the sound signal (212) based on at least one unmixing instruction (214), and an output coupled to the sound unmixer (214) to output the at least one sound stream (216).

Abstract: A frequency band of an audio input signal is analyzed to determine if a transient is present. When transients are detected, modifications are made to the intensity levels corresponding to the frequency band for a brief time period.

Abstract: Systems and methods for processing acoustic signals in vehicles are provided. An example system comprises one or more microphones and a voice monitoring device. The voice monitoring device can receive, via the one or more microphones, an acoustic signal and suppress noise in the acoustic signal to obtain a clean speech component. The obtained clean speech component can be provided to one or more vehicle systems. In some embodiments, two microphones selected from the one or more microphones can be positioned on an inner side of a roof of the vehicle, above a windshield, in front of a driver's seat, and directed towards a driver. The two microphones can be equidistant with respect to a symmetry plane of the driver's seat.

Abstract: Noise suppression is performed based on null coherence between sub-band signals of a primary acoustic signal and a secondary acoustic signal. The null coherence of a signal refers to portions of the signal that have high coherence and can be nullified by a null processor. The nullified component corresponds to target sources, such as an individual speaking into a phone. The coherence values indicate the presence of a target source and are used to suppress noise in portions of a signal that are not dominated by a desired target source. The inter-microphone level difference may be used in combination with the null coherence to provide noise suppression.