A howling removing apparatus according to the present disclosure is a howling removing apparatus to be connected to a microphone and a speaker. The howling removing apparatus includes: a nonlinear converter that nonlinearly converts a sound signal input to the speaker and outputs a nonlinear signal; a delay unit that delays the sound signal by a fixed time and outputs a delay signal; a norm calculator that calculates a norm from the delay signal; a filter coefficient generator that, based on the nonlinear signal, the delay signal and the norm, generates an adaptive filter that simulates a transfer characteristic of a space where the sound signal is reproduced from the speaker and is returned to the microphone; a cancel signal generator that convolves the delay signal and the adaptive filter with each other and generates a cancel signal; and a subtracter that subtracts the cancel signal from the sound signal. When an average sound pressure level of the sound signal exceeds a threshold value, the filter coefficient generator initializes the adaptive filter, thereby sensing oscillation of the adaptive filter even when a positional relationship between the microphone and the speaker is dynamically changed, and returning an output to normal.

A mobile communications device contains at least two microphones. One microphone is designated by a selector to provide a voice dominant signal and another microphone is designated to provide a noise or echo dominant signal, for a call or a recording. The selector communicates the designations to a switch that routes the selected microphone signals to the inputs of a processor for voice signal enhancement. The selected voice dominant signal is then enhanced by suppressing ambient noise or canceling echo therein, based on the selected noise or echo dominant signal. The designation of microphones may change at any instant during the call or recording depending on various factors, e.g. based on the quality of the microphone signals. Other embodiments are also described.

Presented is a method and associated system for suppression of linear and nonlinear echo. The method includes dividing an input signal into several frequency bands in each of a several of time frames. The input signal may include an echo signal. The method further includes multiplying the input signal in each of the several frequency bands by a corresponding echo suppression signal. Calculating the corresponding echo suppression signal may include estimating a power of the echo signal in a particular frequency band as a sum of several component echo powers, each of the several component echo powers due to an excitation from a far-end signal in a corresponding one of the several frequency bands. Calculating the corresponding echo suppression signal may further include subtracting the power of the echo signal in the particular frequency band from a power of the input signal in the particular frequency band.

An audio processor for a video conference system receives an audio signal from content to be shared over a video conference and an audio signal from a network. The audio signal from the shared content and the audio signal from the network are mixed together for output to a speaker. The audio processor may also receive a local audio signal from a microphone. The local audio signal is mixed with the audio signal of the shared to content to generate an outbound signal.

Features are disclosed for measuring and correcting clock drift and propagation delay in an audio system through one or more waveforms embedded in an audio signal. A first device in communication with a speaker may be configured to obtain an audio signal and insert one or more waveforms into the audio signal. For example, the waveforms may be inserted during an interval of time. A second device in communication with a microphone may be configured to detect sound as an audio input signal. The second device may obtain a spectral representation of the audio input signal and determine a rotation based on the spectral representation at the frequency of at least one of the inserted waveforms. Clock drift may be determined based on the rotation.

Features are disclosed for measuring and correcting clock drift and propagation delay in an audio system through one or more waveforms embedded in an audio signal. A first device in communication with a speaker may be configured to obtain an audio signal and insert one or more waveforms into the audio signal. For example, the waveforms may be inserted during an interval of time. A second device in communication with a microphone may be configured to detect sound as an audio input signal. The second device may obtain a spectral representation of the audio input signal and determine a rotation based on the spectral representation at the frequency of at least one of the inserted waveforms. Clock drift may be determined based on the rotation.

The application relates to a communication device, e.g. a speakerphone, comprising a microphone signal path, MSP, and a loudspeaker signal path, SSP, the microphone signal path comprising a microphone unit, an MSP-filter, and a transmitter unit operationally connected to each other and configured to transmit a processed signal originating from an input sound picked up by the microphone, the loudspeaker signal path comprising a receiver unit, an SSP-filter, and a loudspeaker unit operationally connected to each other and configured to provide an acoustic sound signal originating from a signal received by the receiver unit. The communication device comprises a control unit for dynamically controlling the filtering characteristics of the MSP and SSP-filters based on one or more control input signals. This has the advantage of providing a simple and flexible scheme for decreasing echo in a communication device, while ensuring an acceptable sound quality in the transmitted signal.

A method, and one or more non-transitory computer-readable media storing instructions, and a device for removal of unwanted components in an audio signal, the device comprising a processor, coupled to memory, configured to receive reference and processed inputs into memory where the processed input is a result of a reduction process of unwanted components of the audio signal, estimate envelope values for processed and reference inputs at a plurality of time and frequency instances, for each time and frequency instance: compute a first gain in relation to a ratio of the estimated envelope value of the processed input to the estimated envelope value of the reference input, apply a nonlinear process to said first gain to produce a second gain, compute an output gain as the ratio between second gain and first gain and, apply the output gain to processed input, thereby producing a filtered output with unwanted components suppressed.

A method, and one or more non-transitory computer-readable media storing instructions, and a device for removal of unwanted components in an audio signal, the device comprising a processor, coupled to memory, configured to receive reference and processed inputs into memory where the processed input is a result of a reduction process of unwanted components of the audio signal, estimate envelope values for processed and reference inputs at a plurality of time and frequency instances, for each time and frequency instance: compute a first gain in relation to a ratio of the estimated envelope value of the processed input to the estimated envelope value of the reference input, apply a nonlinear process to said first gain to produce a second gain, compute an output gain as the ratio between second gain and first gain and, apply the output gain to processed input, thereby producing a filtered output with unwanted components suppressed.

An acoustic echo cancellation (AEC) system that detects and compensates for differences in sample rates between the AEC system and a set of wireless speakers based on a search-based trial-and-error technique. The system individually determines a frequency offset for each microphone-speaker pair using an iterative process, determining an echo-return loss enhancement (ERLE) value for each offset that is tried, and selecting the frequency offset associated with the largest ERLE value.