According to an aspect, an active noise control device includes: a control frequency determinator that determines a frequency of the noise; a reference cosine-wave generator that generates a reference cosine-wave signal having the determined frequency; a reference sine-wave generator that generates a reference sine-wave signal having the frequency of a noise; a first one-tap adaptive filter to which the reference cosine-wave signal is input, the first one-tap adaptive filter having a first filter coefficient; a second one-tap adaptive filter to which the reference sine-wave signal is input, the second one-tap adaptive filter having a second filter coefficient; an adder that adds an output signal from the first one-tap adaptive filter and an output signal from the second one-tap adaptive filter to each other; a secondary noise generator that is driven by an output signal from the adder to generate a secondary noise; a residual sound detector that detects a residual sound generated by interference between the secondary noise and the noise; a simulation signal generator that outputs a simulation cosine-wave signal and a simulation sine-wave signal, the simulation cosine-wave signal and the simulation sine-wave signal being obtained by correcting the reference cosine-wave signal and the reference sine-wave signal using a characteristic in which a transfer characteristic from the secondary noise generator to the residual sound detector is simulated; and a filter coefficient updating unit that updates the first filter coefficient and the second filter coefficient based on an output signal from the residual sound detector, an output signal from the simulation signal generator, the reference cosine-wave signal, the reference sine-wave signal, and the output signal from the adder. The secondary noise is reduced in a space where the residual sound detector is installed.

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.

An active noise control arrangement has a signal input (SI), a microphone input (MI), a signal output (SO) and a digital interface (DI). A signal processing block (SP) coupled to the microphone input (MI) by means of an amplifier (MA) has a digitally adjustable gain and comprises combining means (CM) and a filter (TP). The signal processing block (SP) is configured to generate an output signal at the signal output (SO) as a function of an input signal at the signal input (SI) and an amplified microphone signal. A control block (CB) is coupled to the digital interface (DI) and configured to adjust the gain of the amplifier (MA).

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.

An audio customization system responsive to one or more audio profiles to define a transformation to apply to audio information. The system allows users to select the profiles that dictate the nature of the customization of the audio delivered to an audio output devise such as a headphones. The system is set up to be able to lessen the influence of undesirable audio and enhance desirable audio. The system may specify aspects of audio to be modified by specification of filtering algorithm, characterization of audio samples, monitored distortion, user selection, location specification or environmental specification and may be used in a social networking system.

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.

A programmable Active Noise Compensation (ANC) system for an audio input includes a parameter store structured to store a number of various filter parameters. A mode of operation is selected that represents the type of environment the ANC system is operating in—feed-forward, feed-back, or combined feed-forward and feedback. Different filter parameters are retrieved from the parameter store based on the selected mode and desired operation. Audio inputs are sampled at a relatively high sample rate that matches inputs from a feed-forward and feedback microphone that may be present in the system. Parameters and instructions may be changed in the system responsive to changing conditions of the compensation system.