Abstract: A mute setting is automatically set based on a speech detection result for acoustic signals received by a device. A device detects the speech based on a variety of cues from acoustic signals received using one or more microphones. If speech is detected within one or more frames, a mute setting may be automatically turned off. If speech is not detected, a mute setting may be automatically turned on. A mute setting may remain on as long as speech is not detected within the received acoustic signals. A varying delay may be implemented to help avoid false detections. The delay may be utilized during a mute-on state, and gradually removed during a transition from a mute-on state to a mute-off state.

Abstract: Systems and methods for adaptive intelligent noise suppression are provided. In exemplary embodiments, a primary acoustic signal is received. A speech distortion estimate is then determined based on the primary acoustic signal. The speech distortion estimate is used to derive control signals which adjust an enhancement filter. The enhancement filter is used to generate a plurality of gain masks, which may be applied to the primary acoustic signal to generate a noise suppressed signal.

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 for utilizing inter-microphone level differences to attenuate noise and enhance speech are provided. In exemplary embodiments, energy estimates of acoustic signals received by a primary microphone and a secondary microphone are determined in order to determine an inter-microphone level difference (ILD). This ILD in combination with a noise estimate based only on a primary microphone acoustic signal allow a filter estimate to be derived. In some embodiments, the derived filter estimate may be smoothed. The filter estimate is then applied to the acoustic signal from the primary microphone to generate a speech estimate.

Abstract: Systems and methods for adaptive power control are provided. In exemplary embodiments, a primary signal is received. A noise power level of the primary signal is then estimated. The noise power level may then be compared to at least one power threshold. Subsequently, a large power consuming system is controlled based on the comparison of the noise power level to the power threshold.

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: Provided are methods and systems for improving quality of speech communications. The method may be for improving quality of speech communications in a system having a speech encoder configured to encode a first audio signal using a first set of encoding parameters associated with a first noise suppressor. A method may involve receiving a second audio signal at a second noise suppressor which provides much higher quality noise suppression than the first noise suppressor. The second audio signal may be generated by a single microphone or a combination of multiple microphones. The second noise suppressor may suppress the noise in the second audio signal to generate a processed signal which may be sent to a speech encoder. A second set of encoding parameters may be provided by the second noise suppressor for use by the speech encoder when encoding the processed signal into corresponding data.

Abstract: An adaptive power control monitors the noise level within a primary acoustic signal and compares the noise level to a threshold. If the noise level is lower than the threshold, a noise suppression system is deactivated and bypass filtering and cross fading are enabled. If the noise level is higher than the threshold, the noise suppression system is activated.

Abstract: The present technology minimizes undesirable effects of multi-level noise suppression processing by applying an adaptive equalization. A noise suppression system may apply different levels of noise suppression based on the (user-perceived) signal-to-noise-ratio (SNR). The resulting high-frequency data attenuation may be counteracted by adapting the signal equalization. The present technology may be applied in both transmit and receive paths of communication devices. Intelligibility may particularly be improved under varying noise conditions, e.g. when a cell phone user is moving in and out of noisy environments.

Abstract: An audio processing system processes an audio signal that may come from one or more microphones. The audio processing system may use information from one or more non-acoustic sensors to improve a variety of system characteristics, including responsiveness and quality. Those audio processing systems that use spatial information, for example to separate multiple audio sources, are undesirably susceptible to changes in the relative position of any audio sources, the audio processing system itself, or any combination thereof. Using the non-acoustic sensor information may decrease this susceptibility advantageously in an audio processing system.

Abstract: Wind noise is detected in and removed from an acoustic signal. Features may be extracted from the acoustic signal. The extracted features may be processed to classify the signal as including wind noise or not. The wind noise may be removed before or during processing of the acoustic signal. The wind noise may be suppressed by estimating a wind noise model, deriving a modification, and applying the modification to the acoustic signal. In audio devices with multiple microphones, the channel exhibiting wind noise (i.e., acoustic signal frame associated with the wind noise) may be discarded for the frame in which wind noise is detected.

Abstract: Systems and methods for controlling adaptivity of signal modification, such as noise suppression, using a phantom coefficient are provided. The process for controlling adaptivity comprises receiving a signal. Determinations may be made of whether an adaptation coefficient satisfies an adaptation constraint and of whether the phantom coefficient satisfies the adaptation constraint. The phantom coefficient may be updated, for example, toward a current observation. The adaptation coefficient may be updated, for example, toward the phantom coefficient, based on whether the phantom coefficient satisfies an adaptation constraint of the signal. A modified signal may be generated by applying the adaptation coefficient to the signal based on whether the adaptation coefficient satisfies the adaptation constraint. Accordingly, the modified signal may be outputted.

Abstract: The present technology provides robust, high quality dereverberation of an acoustic signal which can overcome or substantially alleviate the problems associated with the diverse and dynamic nature of the surrounding acoustic environment. The present technology utilizes acoustic signals received from a plurality of microphones to carry out a multi-faceted analysis which accurately identifies reverberation based on the correlation between the acoustic signals. Due to the spatial distance between the microphones and the variation in reflection paths present in the surrounding acoustic environment, the correlation between the acoustic signals can be used to accurately determine whether portions of one or more of the acoustic signals contain desired speech or undesired reverberation. These correlation characteristics are then used to generate signal modifications applied to one or more of the received acoustic signals to preserve speech and reduce reverberation.

Abstract: Provided are systems and methods for dynamic detection of changes in signal bandwidth associated with audio samples received by a communication device during handovers, where the communication device is moved through geographical areas served by different wireless network technologies. Based on the detection, operation parameters of an audio processor of the communication device may be adjusted or switched to achieve optimal performance of audio enhancing procedures (e.g., noise suppression) performed by the audio processor and to preserve minimal power consumption.

Abstract: Systems and methods for adaptive intelligent noise suppression are provided. In exemplary embodiments, a primary acoustic signal is received. A speech distortion estimate is then determined based on the primary acoustic signal. The speech distortion estimate is used to derive control signals which adjust an enhancement filter. The enhancement filter is used to generate a plurality of gain masks, which may be applied to the primary acoustic signal to generate a noise suppressed signal.

Abstract: Crosstalk reduction and/or cancellation systems and methods are provided herein. In exemplary embodiments, a far-end acoustic signal may be delayed by M samples. Additionally, a crosstalk estimate value for the delayed far-end acoustic signal may be subtracted from an input acoustic signal. The crosstalk estimate value is a scaled version of filter outputs generated by a finite impulse response filter that utilizes predetermined filter coefficients. The filter outputs are scaled using a dynamic gain value.

Abstract: An audio device having two pairs of microphones for noise suppression. Primary and secondary microphones of the three microphones may be positioned closely spaced to each other to provide acoustic signals used to achieve noise cancellation/suppression. A tertiary microphone may be spaced with respect to either the primary microphone or the secondary microphone in a spread-microphone configuration for deriving level cues from audio signals provided by the tertiary and the primary or secondary microphone. Signals from two microphones may be used rather than three microphones. The level cues are expressed via an inter-microphone level difference (ILD) used to determine one or more cluster tracking control signal(s). The ILD based cluster tracking signals are used to control adaptation of null-processing noise suppression modules.

Abstract: An audio processing system processes an audio signal that may come from one or more microphones. The audio processing system may use information from one or more non-acoustic sensors to improve a variety of system characteristics, including responsiveness and quality. Especially those audio processing systems that use spatial information, for example to separate multiple audio sources, are undesirably susceptible to changes in the relative position of any audio sources, the audio processing system itself, or any combination thereof. Using the non-acoustic sensor information may decrease this susceptibility advantageously in an audio processing system.

Abstract: Audio signal bandwidth expansion is performed on a narrow bandwidth signal received from a far end source. The far end source may transmit the signal over the audio communication network. The narrow band signal bandwidth is expanded such that the bandwidth exceeds that of the audio communication network. The signal may be expanded by performing frequency folding on the signal. One or more features are determined for the narrow bandwidth signal, and the expanded signal is modified based on a feature. The feature may be signal band energy slope, narrow band signal energy, or some other feature. The modification may be performed by a shelf filter selected based on the feature. The modified signals are provided for additional processing. In some embodiments, a noise component is added to the narrow band signal prior to folding to create an excitation that reduces the appearance of a fully harmonic signal characteristic.

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.