260 results about "Noise reduction algorithm" patented technology

A noise canceling and communication system is described. An in-ear device is adapted to fit in the ear canal of a device user. A passive noise reduction element reduces external noise entering the ear canal. An external microphone senses an external acoustic signal outside the ear canal to produce a representative external microphone signal. An internal microphone senses an internal acoustic signal proximal to the tympanic membrane to produce a representative internal microphone signal. One or more internal sound generators produce a noise cancellation signal and an acoustic communication signal, both directed towards the tympanic membrane. A probe tube shapes an acoustic response between the internal sound generator and the internal microphone to be relatively constant over a wide audio frequency band. An electronics module is located externally of the ear canal and in communication with the in-ear device for processing the microphone signals using a hybrid feed forward and feedback active noise reduction algorithm to produce the noise cancellation signal. The noise reduction algorithm includes a modeling component based on a transfer function associated with the internal sound generator and at least one of the microphones to automatically adjust the noise cancellation signal for fit and geometry of the ear canal of the user. The communication component also includes a modeling component based on a transfer function associated with the internal sound generator and at least one of the microphones to automatically adjust the communication signal for fit and geometry of the ear canal of the user and to assure that the communication signal does not interfere with the noise reduction algorithm and that the noise cancellation signal does not interfere with passing of the communication signal.

A method of reducing noise by cascading a plurality of noise reduction algorithms is provided. A sequence of noise reduction algorithms are applied to the noisy signal. The noise reduction algorithms are cascaded together, with the final noise reduction algorithm in the sequence providing the system output signal. The sequence of noise reduction algorithms includes a plurality of noise reduction algorithms that are sufficiently different from each other such that resulting distortions and artifacts are sufficiently different to result in reduced human perception of the artifact and distortion levels in the system output signal.

A two microphone noise reduction system is described. In an embodiment, input signals from each of the microphones are divided into subbands and each subband is then filtered independently to separate noise and desired signals and to suppress non-stationary and stationary noise. Filtering methods used include adaptive decorrelation filtering. A post-processing module using adaptive noise cancellation like filtering algorithms may be used to further suppress stationary and non-stationary noise in the output signals from the adaptive decorrelation filtering and a single microphone noise reduction algorithm may be used to further provide optimal stationary noise reduction performance of the system.

This invention describes a practical application of noise reduction in hearing aids. Although listening in noisy conditions is difficult for persons with normal hearing, hearing impaired individuals are at a considerable further disadvantage. Under light noise conditions, conventional hearing aids amplifying the input signal sufficiently to overcome the hearing loss. For a typical sloping hearing loss where there is a loss in high frequency hearing sensitivity, the amount of boost (or gain) rises with frequency. Most frequently, the loss in sensitivity is only for low-level signals; high level signals are affective minimally or not at all. A compression hearing aid is able to compensate by automatically lowering the gain as the input signal level rises. This compression action is usually compromised under noisy conditions. In general, hearing aids are of lesser benefit under noisy conditions since both noise and speech are boosted together when what is really required is a reduction of the noise relative to the speech. A noise reduction algorithm with the dual purpose of enhancing speech relative to noise and also providing a relatively clean signal for the compression circuitry is described.

Systems and methods of improved noise reduction using direction of arrival information include: receiving an audio signal from two or more acoustic sensors; applying a beamformer module to employ a first noise cancellation algorithm to the audio signal; applying a noise reduction post-filter module to the audio signal, the application of which includes: estimating a current noise spectrum of the received audio signal after the application of the first noise cancellation algorithm; using spatial information derived from the audio signal received from the two or more acoustic sensors to determine a measured direction-of-arrival by estimating the current time-delay between the acoustic sensor inputs; comparing the measured direction-of-arrival to a target direction-of-arrival; applying a second noise reduction algorithm to the audio signal in proportion to the difference between the measured direction-of-arrival and the target direction-of-arrival; and outputting a single audio stream with reduced background noise.

System and methods for robust multiple speaker segmentation in noisy conversational speech are presented. Robust voice activity detection is applied to detect temporal speech events. In order to get robust speech features and detect speech events in a noisy environment, a noise reduction algorithm is applied, using noise tracking. After noise reduction and voice activity detection, the incoming audio/speech is initially labeled as speech segments or silence segments. With no prior knowledge of the number of speakers, the system identifies one reliable speech segment near the beginning of the conversational speech and extracts speech features with a short latency, then learns a statistical model from the selected speech segment. This initial statistical model is used to identify the succeeding speech segments in a conversation. The statistical model is also continuously adapted and expanded with newly identified speech segments that match well to the model. The speech segments with low likelihoods are labeled with a second speaker ID, and a statistical model is learned from them. At the same time, these two trained speaker models are also updated/adapted once a reliable speech segment is identified. If a speech segment does not match well to the two speaker models, the speech segment is temporarily labeled as an outlier or as originating from a third speaker. This procedure is then applied recursively as needed when there are more than two speakers in a conversation.

Root cause analysis of changes impacting database performance, availability and continuity to the underlying business may be performed. A system for doing so may include analytical and visual comparison root cause analysis engines to identify changed database performance statistical parameters (such as CPU consumption, physical I/O, etc) related to a historical period of time on a single database or across multiple databases. Result data may be displayed through a series of charts and reports that may indicate whether correlations exist between unexpected database performance and relative changes in database performance statistical parameters. A visual root cause analysis system may further apply noise reduction algorithms to clarify trends in changes of database system performance.

The invention provides a noise reduction method, a device and a mobile terminal. The method and the device are applied to the mobile terminal with double microphones. The noise reduction method comprises the steps that whether the noise of the ambient environment is greater than a predetermined threshold is judged, and a judgment result is acquired; when the judgment result is that the noise of the ambient environment is greater than or equal to the predetermined threshold, a noise reduction algorithm of the double microphones is switched on for noise reduction; when the judgment result is that the noise of the ambient environment is less than the predetermined threshold, the noise reduction algorithm is switched off. According to the technical scheme of the invention, the problem of use inconvenience, which is caused by the fact that the noise reduction algorithm of the double microphones needs manual setting before a conversation, can be effectively solved.

Some embodiments of the invention provide devices, systems and methods of noise identification and cancellation. For example, an apparatus in accordance with an embodiment of the invention includes: a characterizer to estimate a non-linear mutual relation between: a characteristic of an incoming interference signal, and an estimated portion of noise level in an incoming signal-of-interest caused by the incoming interference signal; and a noise canceller to reduce an effect of the interference signal on the signal-of-interest by applying a noise reduction algorithm based on said non-linear mutual relation.

The invention discloses a voice awakening method. The method comprises the following steps: acquiring a voice signal through a microphone array; respectively carrying out denoising processing on the voice signal by utilizing N beam forming denoising algorithms so as to obtain N denoising signals, wherein each beam forming denoising algorithm corresponds to one area in N areas, the areas corresponding to the different beam forming denoising algorithms are different, the union set of the N areas cover a signal acquisition area of the microphone array, and N is a positive integer greater than 1;and carrying out voice awakening according to at least one of the N denoising signals by utilizing an awakening engine. The method is capable of playing the denoising performance of the denoising algorithms to the greatest extent, and eliminating the echoes outside the beams so as to enhance the echo elimination performance, has a relatively high awakening rate and recognition rate, and is simpleto realize and easy to popularize. The invention further discloses a corresponding device, equipment and a medium.

The invention discloses a self-adaptive active noise reduction method for an error-free microphone. According to the method, only one reference microphone is used for achieving active noise reduction;the method comprises the steps that firstly, transfer functions of a main channel and a secondary channel are estimated in advance, an error signal of a target noise-reduction point is estimated in real time by utilizing a received reference signal, and then weight coefficient vectors of a feedforward filter and a feedback filter are updated by using the error signal; the output of the feedforward filter and the output of the feedback filter are superimposed to obtain the output of an infrasound source. By means of the method, an error microphone required in a traditional self-adaptive activenoise reduction method is not needed, the problems of insufficient algorithm stability, complicated earphone structure and the like generally existing in traditional self-adaptive active noise reduction can be solved, and therefore the applicability and stability of the self-adaptive active noise reduction algorithm are greatly improved.

The invention discloses a multi-b-value DWI (diffusion weighted image) noise reduction method based on mutual information. The method comprises steps as follows: acquiring to-be-tested multi-b-value DWIs in multiple diffusion sensitive gradient directions by a magnetic resonance scanner; performing preprocessing such as head movement correction, eddy current correction and the like on the images; estimating different b-value image signal-to-noise ratios; performing noise reduction processing on the images through setting parameters of an optimal noise reduction algorithm so as to acquire DWIs with higher signal-to-noise ratios. The method fully considers features of different images, and three-dimensional structural features of the processed images are reserved as many as possible while noises are eliminated. During establishment of a high-order complex model, different b images have higher image quality, so that acquired high-order parameter images can have smaller errors and fewer singular values. The method can be better applied to preprocessing of a high-order model imaging technology and plays an active role in further research on white matter fiber structures.

The invention discloses a scene adaptive active noise reduction method, which comprises the steps of: picking up environmental noise and converting into a primary noise electrical signal; determiningthe noise type by a noise recognition module; selecting an EQ filter parameter from a filter library to load an active noise reduction module; carrying out adaptive filtration on the primary noise electrical signal by the active noise reduction module to generate a noise reduction electrical signal for exciting a speaker; generating a noise reduction wave by the speaker after receiving the noise reduction electrical signal, and emitting a second canceling audio signal, wherein the noise reduction wave has the same spectral distribution as the primary noise electrical signal, and the sound pressure is the same in magnitude and opposite in phase. A scene adaptive active noise reduction earphone comprises a microphone, a speaker and an active noise reduction module. The noise reduction earphone can adjust the EQ filter parameters in the FXLMS noise reduction algorithm according to the difference of the noise energy concentration region in the noise scene, and cancel the noise which cannotbe removed by the passive noise reduction, thereby improving the effect of the external noise of the scene of the human ear to the earphone export audio.

The invention discloses a self-learning automatic noise reduction system and method of a kitchen ventilator. The system mainly comprises a flue gas turbine, a microphone and a loudspeaker are arranged on the flue gas turbine, after the flue gas turbine is assembled, and before the turbine leaves the factory, the turbine is subject to primary pre-alignment, and the pre-alignment data serve as following field actual running preset benchmark data, and after the flue gas turbine is mounted at the user home, the flue gas turbine runs at the different rotating speeds; flue gas turbine noise and environment noise at different rotating speed segments are collected through the microphone, collected measured data are fed back to a signal processing module on the glue gas turbine, and a self-learning noise reduction algorithm module is combined with the measured data of the flue gas turbine noise and the environment noise to generate a corresponding sound waveform according to the benchmark data preset in a pre-alignment module; the sound waveform is output through the loudspeaker and is used for offsetting most part of noise. The system has the beneficial effects that the main noise component can be effectively removed, a certain obvious effect is achieved, a corresponding sound field and a complex algorithm do not need to be built, and meanwhile, expensive hardware circuit supporting does not need to be built.

The invention discloses a multi-MIC noise reduction method used for a mobile phone. The multi-MIC noise reduction method comprises the steps of S1, determining an MIC, which is closest to a terminal user, from all the MIC of the mobile terminal; S2, setting the MIC closest to the terminal user as a primary MIC and other MIC as secondary MIC; and S3, processing voice signals obtained by the primary MIC and the secondary MIC by using a noise reduction algorithm so as to obtain enhanced voice signals, wherein the step S3 specifically comprises the sub-steps of S301, performing voice endpoint detection for the voice signals obtained by the secondary MIC so as to obtain a pure noise segment and a noise-carrying voice segment, S302, self-adapting to the noise signals in the noise-carrying voice segment by adopting an improved LMS self-adapting filter and regarding the pure noise segment as the input in a least mean square algorithm of noise so as to obtain the voice signals being subjected to the self-adaptive filtration processing, and S303, performing spectral subtraction for the voice signals of the primary MIC and the voice signals being subjected to the self-adaptive filtration processing so as to obtain enhanced voice signals. The multi-MIC noise reduction method used for the mobile phone is good in noise reduction effect and can improve the tone quality of a mobile terminal.

Operation of a patient's heart or lungs may be analyzed by transmitting ultrasound energy into the patient's lung, and detecting Doppler shifts of reflected ultrasound induced by moving borders between blood vessels/soft tissue in the lung and air filled alveoli that surround the blood vessels. Movement of the border is caused by pressure waves in the blood vessels that result in changes in diameter of those blood vessels. The detected Doppler shifts are processed with a noise reduction algorithm, and periodic features in the resulting data are then analyzed to determine the rate of the patient's heartbeat, the rate of the patient's breathing, and/or the appearance of anomalies in the patient's heartbeat

The invention discloses a method and a device for mobile terminal call voice noise reduction. The method comprises steps: time delay for data acquisition by two microphones of the mobile terminal is acquired; according to the calculated time delay, relative positions between the sound source of the mobile terminal user and the two microphones are determined; according to the determined relative positions, the microphone closer to the sound source is set to be the main microphone, and the other one is set to be the auxiliary microphone; and based on the determined main microphone and the auxiliary microphone, noise reduction processing is carried out on the call voice of the mobile terminal through a noise reduction algorithm. Due to the technical scheme of the invention, the sound quality of hands-free call voice receiving is improved in the condition of not increasing the design cost and the space of the mobile communication terminal.

The invention discloses a machine self-learning construction knowledge atlas training method based on a neural network. The method comprises the following steps of acquiring a statement based on a natural situation which is sent by a user, using a voice noise reduction algorithm to carry out filtering noise reduction on an input statement, and determining a matched feedback statement; if the matched feedback statement does not exist, according to a neural network dialogue model, giving an answer of the statement sent by the user, which includes the following steps of constructing a coding layer of a user sending statement model to be a first neural network; analyzing a user sending statement in the first neural network and acquiring a first intermediate vector used for expressing a user sending statement meaning; and constructing a decoding layer of a dialogue generation model to be a second neural network, analyzing the intermediate vector in the second neural network, and acquiring a vector group used for expressing a statement answer. In the invention, a threshold voice noise reduction algorithm is used to acquire a small mean square error and a signal to noise ratio of a reconstruction voice signal is increased.

The invention discloses a method for controlling and wakening a robot through voice, and the method comprises the following steps: adding several microphones at different positions on hardware in a mode of software and hardware combination, so as to judge the source of sound in different directions, wherein the microphones are designed to be provided with corresponding array sound recording parts in software, and carry out analysis and voice processing through built-in voice processing chips; adding a noise reduction algorithm in the software design, so as to process the sound recorded by the microphones; adding the technology of voice recognition and processing and semantic comprehension and extraction in the software design; and adding a logic processing unit linked with the operation of the robot in the software design. Through the above improvement, the method employs the plurality of microphones to record sound in different directions, and improves the sound quality through the noise reduction algorithm. The impact on the method from environment noise is reduced, and the recognition rate and wakening rate are improved. The method is simple in design, is easy to implement, and can product the qualitative leap of the effect of man-machine interaction experience.

The invention discloses a broadband and narrowband hybrid active noise reduction algorithm suitable for a vehicle, and the algorithm comprises the steps: 1, obtaining a system reference noise signal and the frequency of a narrowband component in the system reference noise signal, and obtaining internal reference signals of a sine noise separator and a narrowband active noise reduction system; 2, obtaining a reference signal of a broadband active noise reduction subsystem according to the internal reference signals, and obtaining an output signal of the broadband active noise reduction subsystem according to the reference signal of the broadband active noise reduction subsystem; obtaining an output signal of a narrowband active noise reduction subsystem according to the internal reference signal; and 3, summing the output signal of the broadband active noise reduction subsystem and the output signal of the narrowband active noise reduction subsystem to obtain an output signal of the broadband and narrowband hybrid active noise reduction system.

A method and system for removing acoustic noise from human speech, wherein noise is removed from the speech regardless of its type, magnitude, or direction. The system includes a microphone and a voice activity detection (VAD) data stream coupled to the processor. The microphone receives the sound signal, and the VAD produces a binary 1 when there is speech (voiced and unvoiced), and a 0 when there is no speech. The processor includes a noise reduction algorithm capable of generating a transfer function. These transfer functions include transfer functions generated in response to a determination that vocalization information is missing from the received audio signal within a defined period of time. The transfer functions also include a transfer function generated in response to a determination that vocalization information is present in the audio signal received within a defined period of time. A transfer function is used to generate at least one noise-reduced speech data stream.