A signal processing device according to an embodiment includes: two or more microphones each provided with a sound collection unit directed to an outside of a housing including a driver unit, a control unit (102b) that performs a hearing control of sound output from the driver unit to a listener based on sound signals collected and output by the two or more microphones, and an adjustment unit (200) that adjusts a degree of the hearing control between a degree for wind and a degree for non-wind based on a correlation between the sound signals.

A method that is accomplished in a vehicle engine harmonic modification system. A non-zero target engine harmonic signal that is representative of a target engine harmonic sound level in the vehicle cabin is provided. The target engine harmonic signal is used in an operation of the engine harmonic modification system so as to modify the level of engine harmonic sound in the vehicle cabin, to bring the engine harmonic sound level in the vehicle cabin closer to the target engine harmonic sound level.

Noise signals are captured from one or more physical error microphones located at first locations within the vehicle. High-frequency noise signals are captured from a feedforward system sensor. A virtual microphone algorithm is utilized to estimate noise signals at a virtual location based on the noise signals, the estimation utilizing a transfer function that estimates a signal that would have been received by the one or more physical error microphones at the virtual location. The virtual microphone algorithm is utilized to estimate noise signals at the virtual location based on the high-frequency noise signal. A noise-cancelling signal is provided to cancel noise at the virtual location, the noise-cancelling signal accounting for the noise captured by both the feedforward system sensor and the one or more physical error microphones, the ANC system utilizing a working frequency for the ANC of at least 2 kHz.

A method for masking a vehicle noise includes setting a noise-generated driving region in which the vehicle noise is generated while a vehicle is running; measuring the vehicle noise in the noise-generated driving region; determining a masking sound which cancels the vehicle noise based on the measured vehicle noise; and outputting the determined masking sound into an interior of the vehicle.

A system has a sound generator (20) that generates superimposed sound to a sound to be manipulated. An error sensor (50) measures sound and outputs a corresponding feedback signal (e′(n)). A signal generator (91) generates a sound signal (y(n)). A controller (92) generates a control signals (λ.sub.1(n)) and (λ.sub.2(n)). The adder (94) subtracts one control signal (λ.sub.2(n)) from the feedback signal (e′(n)) and outputs a modified feedback signal (en(n)) to the signal generator (91). A weighter (95) weights the sound signal (y(n)) with the control signal (λ.sub.1(n)) and outputs the weighted sound signal (y′(n)). The generated sound signal (y(n)) is a function of the modified feedback signal (e(n)). The controller (92) generates the control signals (λ.sub.1(n), λ.sub.2(n)) such that a value of the amplitudes of the feedback signal (∥e′(n)∥) corresponds to a predefinable value (Δ).

A system includes a sound generator (20) that generates sound superimposed to sound to be manipulated. An error sensor (50) measures superimposed sound and outputs a corresponding feedback signal (e′(n)). A signal generator (91) generates a sound signal (y(n)). A controller (92) generates a control signal (λ(n)) representing a value of a sequence of rational numbers. A weighter (93) weights the generated sound signal (y(n)) with the control signal (λ(n)) and inverts it. An adder (94) adds the weighted/inverted sound signal to the feedback signal (e′(n)) and outputs a modified feedback signal (e(n)) to the signal generator (91). A weighter (95) weights the generated sound signal (y(n)) with the difference from one and with the control signal (λ(n)) and outputs the sound signal y′(n). The generated sound signal (y(n)) is a function of the modified feedback signal (e(n)).

The disclosure is related to a method and a system for self-tuning active noise canceller (STANC) and a headset apparatus. The headset apparatus is placed on a measurement device to emulate user scenario where the user receives audio signal from the headset. The STANC system receives environmental noise signal from a microphone inside the headset. The output of the STANC system acts as reverse noise signal to suppress the environmental noise signal via a speaker unit. The corresponding mixture signal is defined as an error signal. In a calibration mode, the error signal received from the measurement device can be used to update the STANC parameters. The process will be done when the error signal is lower than a predefined threshold. The final parameters can be saved as default settings for the headset apparatus in a user mode.

Provided are a signal processing device, a program, a range hood device, and a selection method for frequency bins in a signal processing device with which it is possible to reduce the load on computation processing for computing filter coefficients and provide an excellent muting effect even when there are a peak band and a notch band in transmission characteristics from a speaker to an error microphone. A parameter setter sets an update parameter μ such that a filter coefficient W is corrected, only for a first frequency bin that corresponds to a frequency band of a first noise and a second frequency bin that corresponds to a frequency band of a second noise.

The noise suppression method of individual active noise reduction system comprises the steps that: (1) initial noise digital signals are received and converted to serve as input signals of a BP neural network; (2) the input signals are processed to generate secondary digital signals; (3) the secondary digital signals are output to a loudspeaker and secondary noise is generated; (4) remained noise digital signals obtained by overlapping the initial noise and the secondary noise are received; whether remained noise digital signals is continuously constant for the set times is judged; if yes, the secondary digital signals are kept outputting; (5) if not, BP neural network parameters are optimized and adjusted with the amplitude of the remained noise digital signals being minimum as the optimality principle; remained noise digital signals of previous step are served as new input signals and the step (2) is executed again.

According to one embodiment, a noise reduction system for reducing noise including impact noise repetitively generated at a time interval includes the following elements. The error signal generator generates an error signal based on the noise being detected. The delay signal generator has a time delay characteristic and delays a signal, which is generated based on the error signal, to generate a delay signal, the time delay characteristic being determined based on an imaging sequence or pre-scanning by the MRI device and corresponding to the time interval. The control filter generates the first control signal from the delay signal. The loudspeaker unit includes at least one pair of a first filter and a control loudspeaker and a transmission unit.