A method is explained for any adaptive processor processing digital signals by adjusting signal weights on digital signal(s) it handles, to optimize adaptation criteria responsive to a functional purpose or externalities (transient, temporary, situational, and even permanent) of that processor. Adaptation criteria for the adaptive algorithm may be any combination of a signal or parameter estimation, and measured quality(ies). This method performs a linear transformation adapting parameters from M to (M.sub.1+L) dimensions in each adaptation event, such that M.sub.1 weights are updated without constraints and M.sub.0=M?M.sub.1 weights are forced by soft constraints into an L-dimensional subspace they spanned at the beginning of the adaptation period. The same dimensionality reduction, using the same linear transformation, is applied to the input data. The reduced-dimensionality weights are then adapted using the identical optimization strategy employed by the processor, except with input data that has also been reduced in dimensionality.

The present disclosure provides a permanent-magnet fault-tolerant in-wheel motor based on an active sensorless strategy and drive and design methods thereof. The present disclosure proposes the permanent-magnet fault-tolerant in-wheel motor drive system based on an active sensorless strategy by considering sensorless operation performance in advance in a motor design stage. The present disclosure adopts fractional-slot concentrated windings, and ingeniously arranges alternating poles, a multi-layer magnetic barrier, and auxiliary permanent magnets, thus improving a sensorless operation accuracy of the motor while ensuring fault tolerance of the motor. The present disclosure proposes a frequency-band-adaptive secondary harmonic suppression strategy at a control layer to suppress an influence of a secondary salient harmonic on position observation and improve dynamic response performance of a system.

An audio input signal is digitized via circuitry of an ear-wearable device. An adaptive feedback canceller has an adaptive filter producing an output that is inserted into the digitized audio input signal to cancel feedback. A motion detector provides a motion signal indicative of motion of the car-wearable device. A processor is operable to determine a change in a feedback path based on the motion signal. The processor causes the adaptive filter to have faster adaption in response to the change in the feedback path is above a first threshold. The processor also causes the adaptive filter to have slower adaption in response to the change in the feedback path being below a second threshold.

Example embodiments disclosed herein relate to filter coefficient updating in time domain filtering. A method of processing an audio signal is disclosed. The method includes obtaining a predetermined number of target gains for a first portion of the audio signal by analyzing the first portion of the audio signal. Each of the target gains is corresponding to a linear subband of the audio signal. The method also includes determining a filter coefficients for time domain filtering the first portion of the audio signal so as to approximate a frequency response given by the target gains. The filter coefficients are determined by iteratively selecting at least one target gain from the target gains and updating the filter coefficient based on the selected at least one target gain. Corresponding system and computer program product for processing an audio signal are also disclosed.

A partitioned block frequency domain adaptive filter device includes a frequency domain adaptive filter configured for filtering a frequency domain representation of a time domain input signal depending on a set of filter coefficients consisting of a plurality of blocks of filter coefficients in order to produce a filtered signal; a plurality of parallel arranged filter update blocks; wherein each of the filter update blocks includes an adaptation module configured for executing an adaptation sequence including the steps of calculating an approximation of a constrained gradient update for the filter coefficients of the respective block of filter coefficients, and calculating a cumulative error introduced on the unconstrained gradient update; wherein each of the filter update blocks includes a correction module configured for executing a correction sequence including the steps of calculating a corrected constrained gradient update for the filter coefficients of the respective block of filter coefficients.

A method and an apparatus for determining a stability factor of an adaptive filter is presented. The method includes: determining, according to first input signal that are input to an adaptive filter, a reference input matrix of the first input signal; determining a stability parameter of the first input signal according to the reference input matrix; and determining a stability factor of the adaptive filter according to the stability parameter. According to the method and apparatus for determining a stability factor of an adaptive filter provided in the embodiments of the present application, the stability factor of the adaptive filter can be adaptively obtained according to a stability feature of the first input signal, and the adaptive filter can reach a balance between a convergence speed and steady state error performance.

A method is explained for any adaptive processor processing digital signals by adjusting signal weights on digital signal(s) it handles, to optimize adaptation criteria responsive to a functional purpose or externalities (transient, temporary, situational, and even permanent) of that processor. Adaptation criteria for the adaptive algorithm may be any combination of a signal or parameter estimation, and measured quality(ies). This method performs a linear transformation adapting parameters from M to (M.sub.1+L) dimensions in each adaptation event, such that M.sub.1 weights are updated without constraints and M.sub.0=M?M.sub.1 weights are forced by soft constraints into an L-dimensional subspace they spanned at the beginning of the adaptation period. The same dimensionality reduction, using the same linear transformation, is applied to the input data. The reduced-dimensionality weights are then adapted using the identical optimization strategy employed by the processor, except with input data that has also been reduced in dimensionality.

An adaptive processor is configured to provide for reduced-complexity estimation of signal and data modes in high-dimensional data sets by implementing subspace-constrained partial updates to optimize an eigenvalue-based objective function. The adaptive processor selects, from a set of combiner weights, a set of update weights and a set of held weights; performs updates to the set of held weights within a reduced-dimensionality subspace and unconstrained updates to the set of update weights to produce updated combiner weights; and employs the updated combiner weights to determine at least one solution to an eigenequation or pseudo-eigenequation.

In one embodiment, a method for training an adaptive filter includes receiving, by a processor from a device, an input signal and a training reference signal and determining a correlation matrix in accordance with the input signal, the training reference signal, and a filter type. The method also includes determining a plurality of coefficients in accordance with the correlation matrix and adjusting the adaptive filter in accordance with the plurality of coefficients.

A method and an apparatus for determining a stability factor of an adaptive filter is presented. The method includes: determining, according to first input signal that are input to an adaptive filter, a reference input matrix of the first input signal; determining a stability parameter of the first input signal according to the reference input matrix; and determining a stability factor of the adaptive filter according to the stability parameter. According to the method and apparatus for determining a stability factor of an adaptive filter provided in the embodiments of the present application, the stability factor of the adaptive filter can be adaptively obtained according to a stability feature of the first input signal, and the adaptive filter can reach a balance between a convergence speed and steady state error performance.