An active noise reduction system and method, and a storage medium are provided. In the system, a first signal acquisition circuitry acquires an external noise signal at a noise cancellation spot, and transmits the acquired external noise signal to a noise control system including a first frequency nonlinear transformation circuitry, a first filter circuitry and an inverter. The first frequency nonlinear transformation circuitry receives the external noise signal, and expands at least one target frequency band of the external noise signal based on a frequency nonlinear transformation mapping function to generate a first transformed external noise signal, the first filter circuitry filters the first transformed external noise signal to generate a filtered external noise signal, and the inverter performs inversion on the filtered external noise signal to generate a noise cancellation signal; and the signal output circuitry receives and outputs the noise cancellation signal to cancel an actual noise.

A digital signal processing system for multiplying a digital value and a digital signal. The digital signal processing system receives the digital value in an encoded format, and multiplies the digital value with the digital signal. The digital value in the encoded format has an offset, which is encoded as a floating point. The disclosure provides a digital processing system that can carry out a multiplication operation with a smaller area, less complexity and/or reduced power usage compared with known multipliers.

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.

A method for hybrid noise suppression includes receiving a processed audio signal from an audio device. The processed audio signal results from a partial audio processing performed on a noisy audio input signal. The method further includes predicting a noise suppression parameter using a neural network model operating on the processed audio signal and generating a noise-suppressed audio signal from the processed audio signal, using the noise suppression parameter. The method further includes generating a noise-suppressed audio output signal from the noise-suppressed audio signal using an additional audio processing and outputting the noise-suppressed audio output signal.

A computer-implemented method for generating anti-noise using an anti-noise generator to suppress noise from a noise source in an environment comprises processing a sound signal, which is representative of ambient sound including noise, anti-noise and propagation noise from the environment, using a deep learning algorithm configured to generate an anti-noise signal to form anti-noise. The deep learning algorithm comprises a convolution layer; after the convolution layer, a series of atrous scaled convolution modules, wherein each of the atrous scaled convolution modules comprises an atrous convolution, a nonlinear activation function after the atrous convolution, and a pointwise convolution after the nonlinear activation function; after the series of atrous scaled convolution modules, a recurrent neural network; and after the recurrent neural network, a plurality of fully connected layers.

A method, system, and apparatus for noise cancelation is disclosed, which may be used in a wireless unit (WU). The WU may include a processor, a memory, a user interface, one or more internal microphones and internal speakers. A removably connected headset may include microphones and speakers. The WU may receive a first signal from the headset. The WU may receive a first ambient noise at the one or more internal microphones, and the WU may generate a second signal based on the first ambient noise. The WU may calculate an estimate of ambient noise based on the first and second signals, calculate a signal for noise cancellation based on the estimate, cancel estimated ambient noise from an audio output signal based on an application of the signal for noise cancellation, and send the audio output signal to one or more speakers of the headset or of the WU.

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.

A method for hybrid noise suppression includes receiving a processed audio signal from an audio device. The processed audio signal results from a partial audio processing performed on a noisy audio input signal. The method further includes predicting a noise suppression parameter using a neural network model operating on the processed audio signal and generating a noise-suppressed audio signal from the processed audio signal, using the noise suppression parameter. The method further includes generating a noise-suppressed audio output signal from the noise-suppressed audio signal using an additional audio processing and outputting the noise-suppressed audio output signal.

A method for hybrid noise suppression involves receiving a first processed audio signal and a second processed audio signal from an audio device. The first processed audio signal results from a comprehensive audio processing including a noise spectrum estimate-based noise suppression performed on a noisy audio input signal obtained by the audio device. The second processed signal results from a partial audio processing excluding the noise spectrum estimate-based noise suppression performed on the noisy audio input signal. The method further involves temporally aligning the second processed audio signal with the first processed audio signal, predicting a noise suppression parameter using a neural network model operating on the second processed audio signal after the temporal alignment, generating a noise-suppressed audio output signal from the first processed audio signal after the temporal alignment using the noise suppression parameter, and outputting the noise-suppressed audio output signal.

A method of operating a headphone configured to be removed from and placed in close proximity to a user's ear can include generating an input signal by an input signal generating device. The method can also include determining whether an insertion event has occurred based on the generated input signal and causing the headphone to operate in 5 a low power mode responsive to an absence of an insertion event determination after a first period of time. The method can also include causing the headphone to operate in an ultra-low power mode responsive to the absence of an insertion event determination after a second period of time that occurs after the first period of time, the ultra-low power mode having a lower power consumption than the low power mode.