Abstract:
A system configured to improve noise cancellation by using portions of multiple reference signals instead of using a complete reference signal. The system divides a frequency spectrum into frequency bands and selects a single reference signal from a group of potential reference signals for every frequency band. For example, a first reference signal is selected for a first frequency band while a second reference signal is selected for a second frequency band. The system may generate a combined reference signal using portions of each of the selected reference signals, such as a portion of the first reference signal corresponding to the first frequency band and a portion of the second reference signal corresponding to the second frequency band. Additionally or alternatively, the system may perform noise cancellation using each of the selected reference signals and filter the outputs based on the corresponding frequency band to generate combined audio output data.
Abstract:
An echo cancellation system that performs audio beamforming to separate audio input into multiple directions and determines a target signal and a reference signal from the multiple directions. For example, the system may detect a strong signal associated with a speaker and select the strong signal as a reference signal, selecting another direction as a target signal. The system may determine a speech position and may select the speech position as a target signal and an opposite direction as a reference signal. The system may create pairwise combinations of opposite directions, with an individual direction being selected as a target signal and a reference signal. The system may select a fixed beamformer output for the target signal and an adaptive beamformer output for the reference signal, or vice versa. The system may remove the reference signal (e.g., audio output by the loudspeaker) to isolate speech included in the target signal.
Abstract:
Features are disclosed for performing acoustic echo cancellation using random noise. The output may be used to perform speech recognition. Random noise may be introduced into a reference signal path and into a microphone signal path. The random noise introduced into the microphone signal path may be transformed based on an estimated echo path and then combined with microphone output. The random noise introduced into the reference signal path may be combined with a reference signal and then transformed. In some embodiments, the random noise in the reference signal path may be used in the absence of another reference signal, allowing the acoustic echo canceler to be continuously trained.
Abstract:
Features are disclosed for estimating a noise level using a variable step size. An acoustic echo canceller (AEC) may be configured to perform echo cancellation. The acoustic echo canceller may determine an estimated echo using a playback signal. The acoustic echo canceller also may determine an estimated error using the estimated echo and a microphone signal. A variable step size may be determined using the estimated error and the microphone signal. Noise reduction may be performed using the variable step size.
Abstract:
Techniques for improving beamforming using filter coefficient values corresponding to virtual microphones are described. A system may define “virtual” microphone positions and determine corresponding filter coefficient values. These filter coefficient values may be applied to input audio data captured by actual physical microphones, enabling the system to improve performance of beamforming and/or to reduce a number of physical microphones without degrading performance. Offline testing and simulations may be performed to identify the best combination of virtual microphones and/or filter coefficient values for a particular look-direction. For example, the simulations may identify that a first filter coefficient corresponding to a first virtual microphone and a first direction will be associated with a first physical microphone and the first direction. During run-time processing, a device may generate beamformed audio data for the first direction by applying the first filter coefficient to input audio data captured by the first physical microphone.
Abstract:
A multi-channel echo cancellation system that dynamically adapts to changes in acoustic conditions. The system does not require a sequence of “start-up” tones to determine the impulse responses. Rather, the adaptive filters approximate estimated transfer functions for each channel. A secondary adaptive filter adjusts cancellation to adapt to changes in the actual transfer functions over time after the adaptive filters have been trained, even if the reference signals are not unique relative to each other.
Abstract:
An echo cancellation system that detects and compensates for differences in sample rates between the echo cancellation system and a set of wireless speakers based on a frequency-domain analysis. The system generates Fourier transforms for a microphone signal and a reference signal and determines a series of angles for individual frames. For each tone in the Fourier transforms, the system determines the angles and uses linear regression to determine an individual frequency offset associated with the tone. Using the individual frequency offsets associated with the tones, the system uses linear regression to determine an overall frequency offset between the audio sent to the speakers and the audio received from a microphone. Based on the overall frequency offset, samples of the audio are added or dropped when echo cancellation is performed, compensating for the frequency offset.
Abstract:
An echo cancellation system that detects and compensations for differences in sample rates between the echo cancellation system and a set of wireless speakers based on a frequency-domain analysis of estimated impulse response coefficients. The system tracks the real and imaginary number components of the coefficients, and determines a “rotation” of the coefficients over time caused by a frequency offset between the audio sent to the speakers and the audio received from a microphone. Based on the rotation, samples of the audio are added or dropped when echo cancellation is performed, compensating for the frequency offset.
Abstract:
A acoustic echo canceller (AEC) system may be configured to perform echo cancellation in the frequency domain. Features are disclosed for determining an estimated echo in the frequency domain using adaptive filters. An adaptive filter corresponding to a frequency bin can comprise a plurality of filter taps. Additional features are disclosed for updating the adaptive filter. In addition, a frequency-bin dependent step size controller may be used to control a step size used in updating the adaptive filters. Features are disclosed for determining the frequency-bin dependent step size.
Abstract:
An acoustic echo cancellation (AEC) system that detects and compensates for differences in delay times between the AEC system and a set of wireless speakers. The filter coefficients used for AEC are adjusted based on the determined delay time to correct for frequency domain signal rotation.