Various implementations include a method for implementing a computational architecture for a personal active noise reduction (ANR) device. A method includes receiving a source audio stream with a first DSP and performing ANR on the source audio stream utilizing operational parameters stored in the first DSP; outputting a processed audio stream from the first DSP; generating state data with a second DSP in response to an analysis of at least one of the source audio stream, microphone inputs and the processed audio stream, and communicating signals to the first DSP over a common bus coupled to the first and second DSPs to alter the operational parameters in the first DSP; and utilizing a general purpose processor coupled to both the first DSP and the second DSP to communicate control signals with a communication interface, process state data from the second DSP, and alter the operational parameters in the first DSP.

An active noise reduction device includes: a reference signal input terminal that receives a reference signal from a reference signal source attached to an automobile; a simulated vibration transfer characteristics filter unit that generates a second signal by correcting, using simulated vibration transfer characteristics, a first signal for outputting, from a loudspeaker attached to the automobile, a sound different from a canceling sound, the simulated vibration transfer characteristics simulating vibration transfer characteristics from the loudspeaker to the reference signal source; a first subtracter that outputs a corrected reference signal obtained by subtracting the second signal generated, from the reference signal received by the reference signal input terminal; and an adaptive filter unit that applies an adaptive filter to the corrected reference signal outputted from the first subtracter to generate a canceling signal to be used to output the canceling sound.

A noise controlling method, comprising: generating one reference signal representing a primary noise; generating one secondary noise in response to a control signal, for cancelling the primary noise; generating one error signal representing a superposition of the primary noise and the secondary noise at a position. The method further comprises: generating at least one additional reference signal, and/or at least one additional secondary noise, and/or at least one additional error signal; and generating the control signal(s) for generating the secondary noise(s), by executing an adaptive subband filtering algorithm based on the reference signal(s) and the error signal(s); wherein the step of generating the control signal(s) comprises: decomposing the reference signal(s) and the error signal(s) into subband reference signal(s) and subband error signal(s), respectively, for each subband of a plurality of subbands; updating a subset of one or more subband adaptive filters for at least one subband of the plurality of subbands, based on a subset of the subband reference signal(s) of the at least one subband and a subset of the subband error signal(s) of the at least one subband, wherein at least one of said three subsets is a proper subset; updating at least one fullband adaptive filter based on the updated subband adaptive filter(s); generating the control signal(s) by filtering the reference signal(s) by the updated at least one fullband adaptive filter.

Various implementations include a computational architecture for a personal active noise reduction (ANR) device. The device includes a communication interface that receives an audio stream, a driver, a microphone system and an ANR processing platform. The platform includes a first DSP configured to: receive the audio stream and signals from the microphone system, perform ANR on the audio stream according to a set of parameters in the first DSP, and output a processed audio stream. The platform includes a second DSP configured to: generate state data in response to an analysis of the source audio stream, signals from the microphone system, and the processed audio stream; and alter the operational parameters on the first DSP. The platform includes a general purpose processor configured to: communicate control signals with the communication interface, process state data from the second DSP, and alter the parameters on the first DSP.

An active noise reduction device includes: an adaptive filter that generates a cancelling signal by applying an adaptive filter to a reference signal that has a correlation with noise in a space in an automobile, the cancelling signal being used to output a cancelling sound for reducing the noise; and a filter coefficient updater that calculates a coefficient of the adaptive filter based on a predetermined update equation. At a first timing at which the output of the cancelling sound is started, the filter coefficient updater uses a first coefficient as an initial value of the update equation, the first coefficient being the coefficient of the adaptive filter calculated by the filter coefficient updater at a second timing that is prior to the first timing.

A noise cancellation circuit includes: a first filter circuit for filtering a first input signal according to a first filter coefficient to generate a first filtered signal; a signal processing circuit for generating a feedback signal according to a second input signal and an audio signal; a second filter circuit for filtering the feedback signal according to a second filter coefficient to generate a second filtered signal; a first multiplication circuit for multiplying the first filtered signal by a first scale to generate a first intermediate signal; a second multiplication circuit for multiplying the second filtered signal by a second scale to generate a second intermediate signal; a first adder circuit for adding the first intermediate signal to the second intermediate signal to generate a noise cancellation signal; and a second adder circuit for adding the noise cancellation signal to the audio signal to generate an output signal.

Various implementations include a computational architecture for a personal active noise reduction (ANR) device. The device includes a communication interface that receives an audio stream, a driver, a microphone system and an ANR processing platform. The platform includes a first DSP configured to: receive the audio stream and signals from the microphone system, perform ANR on the audio stream according to a set of parameters in the first DSP, and output a processed audio stream. The platform includes a second DSP configured to: generate state data in response to an analysis of the source audio stream, signals from the microphone system, and the processed audio stream; and alter the operational parameters on the first DSP. The platform includes a general purpose processor configured to: communicate control signals with the communication interface, process state data from the second DSP, and alter the parameters on the first DSP.

An input signal representative of an undesired acoustic noise in a region is captured by one or more first sensors and processed to generate a cancellation signal. An output signal is generated based on the cancelation signal to cause one or more acoustic transducers to cancel, at least in part, the undesired acoustic noise in the region. A feedback signal representative of residual acoustic noise in the region is captured by one or more second sensors. A characteristic of each of the feedback signal, the cancellation signal, and a combination of the cancellation signal and the feedback signal is determined. One or more thresholds are compared to a ratio of (i) the characteristic of the combination of the cancellation signal and the feedback signal and (ii) a combination of the characteristic of the feedback signal and the characteristic of the cancellation signal to determine a convergence state.

Active Noise Cancellation (ANC) systems and methods that reduce latency to improve performance. In certain embodiments the systems sample a noise signal using a sample period to create a stream of digital signal data that is representative of the noise signal. A data transport layer carries the digital signal data to a signal processor. The transport layer temporally organizes the digital signal data to place the digital signal data within an initial phase of a sample period. The remaining phase of the sample period is set to a duration that allows the signal processor to process the digital signal data carried in the initial phase and to output the processed data during the same sample period. In this way, the processing of data occurs within one sample period and the latency is reduced and predictable.

Engine order cancellation (EOC) systems generate feed forward noise signals based on the engine or other rotating shaft RPM and use those signals and adaptively configured W-filters to reduce the in-cabin SPL by radiating anti-noise through speakers. An EOC system may include a drive mode detector for detecting different vehicle drive modes based on an analysis of signals indicative of current vehicle operating conditions. Upon detection, the EOC system may adaptively adjust various tuning parameters for the EOC algorithm based on the current vehicle drive mode. The EOC system may also selectively target different sets of engine orders for noise cancellation according to the current vehicle drive mode based on which engine orders are dominant during that drive mode.