153results about "Multi-channel direction finding" patented technology

An apparatus for detecting sound includes a plurality of microphones, a sound inspecting unit, a direction estimating unit, a background noise removing unit, and an alerting unit. The microphones are used to collect sounds around a user. The sound inspecting unit is used to calculate the feature values of a background noise within a preset time interval, and to determine if a latest collected sound satisfies a preset condition. When the preset condition is satisfied, the direction estimating unit is used to estimate the occurrence direction of the latest collected sound, and to determine if the occurrence direction is within a preset range behind the user. When the preset range is satisfied, the background noise removing unit is used to remove the background noise in the latest collected sound so as to obtain a detected sound. The alerting unit is used to inform the user of the detected sound via an alert message. A method for detecting sound is also disclosed.

A method of signal processing includes calculating a range based on information from a reflected ultrasonic signal. Based on the calculated range, one among a plurality of direction-of-arrival (DOA) estimation operations is selected. The method also includes performing the selected operation to calculate an estimated direction of arrival (DOA) of an audio-frequency component of a multichannel signal. Examples of DOA estimation operations include operations based on phase differences between channels of the multichannel signal and operations based on a difference in gain between signals that are based on channels of the multichannel signal.

An acoustic array platform comprising multiple spaced microphones and associated processing electronics/algorithms projects distinct beams in a chosen look direction across which moving objects such as vehicles pass. These moving objects are accurately detected, classified and counted at extended ranges. The acoustic microphone array employs optimized beamforming to create specially focused listening directions that function as a “motion detector” and “trip line counter”. Algorithms detect and confirm the appropriate presence of objects moving through the beams and perform other algorithmic tests to verify that the object is a valid object to be counted. The proposed approach is realized with modest sized acoustic arrays and a reasonable number of microphones by employing an adaptive beamforming algorithm that achieves enhanced directivity in a principal look direction and which significantly reduces the effects of interferers outside the precise steering direction of the “trip line”.

A method of localizing signals utilizing auto-correlation functions and cross correlation functions includes: collecting data at a plurality of input channels; filtering the data collected from the plurality of input channels in order to identify a primary signal; identifying at least two lags and at least one reference lag in a function of the data; and estimating relative travel times of the data.

A sound processing apparatus includes a sound collection position calculating unit configured to calculate sound collection positions of sound signals of multiple channels on the basis of the sound signals, a sound source direction calculating unit configured to calculate a sound source direction on the basis of the sound signals of multiple channels, a first transfer function calculating unit configured to calculate a first transfer function corresponding to the sound source direction on the basis of the sound signals of multiple channels, and a second transfer function calculating unit configured to calculate a second transfer function by interpolating the first transfer function corresponding to each of a plurality of sound source directions.

A gunshot detection system having one or more sensors detecting indoor shootings and reporting gunshot detection to a central monitoring system. Each sensor includes a microphone and comparison circuit for processing detected sounds and determining whether the sound is indicative of gunshot. If a gunshot is detected, the sensor reports the detection to nearby sensors and to the central system. The central system can interface with third party systems, such as law enforcement communication systems, to provide particularized information to assist first responding in dealing appropriately with situation.

A microphone array system includes a camera that images a picture for a target place, a microphone array that picks up sound using a plurality of microphones, a sound source analyzer that calculates a sound source position based on picked up sound data, an image displaying processor that displays image data, which includes sound source position display indicative of an imaged target place picture and the sound source position on the display, a user-input apparatus that receives an instruction of a specified spot in the target place picture displayed on the display, a directivity calculator that forms sound directivity in a direction which faces the specified spot from the microphone array based on the picked up sound data, and generates the sound data acquired by emphasizing sound in a direction having the directivity, and a sound outputter that outputs the sound data to the speaker and reproduces the sound data.

The invention provides a signal processing device, a signal processing method and program thereof. The signal processing apparatus includes: a learning processing unit that finds a separating matrix for separating mixed signals in which outputs from a plurality of sound sources are mixed, by a learning process that applies ICA (Independent Component Analysis) to observed signals including the mixed signals; a separation processing unit that applies the separating matrix to the observed signals to separate the mixed signals and generate separated signals corresponding to each of the sound sources; and a sound source direction estimating unit that computes a sound source direction of each of the generated separated signals. The sound source direction estimating unit calculates cross-covariance matrices between the observed signals and the separated signals in corresponding time segments in time-frequency domain, computes phase differences between elements of the cross-covariance matrices, and computes a sound source direction corresponding to each of the separated signals by applying the computed phase differences.

An autonomous sonar system and method provide an arrangement capable of beamforming in three dimensions, detecting loud targets, adaptively beamforming in three dimensions to avoid the loud targets, detecting quiet targets, localizing the loud or quiet targets in range, bearing, and depth, detecting modulation of noise associated with propellers of the loud or quiet targets, generating three dimensional tracks of the loud or quiet targets in bearing, range and depth, making classification of the loud or quiet targets, assigning probabilities to the classifications, and generating classification reports according to the classifications for communication to a receiving station, all without human assistance.

A method and apparatus for robust speaker localization and a camera control system employing the same are provided. The apparatus for speaker localization includes: a difference spectrum obtaining section which obtains a difference spectrum of a first pseudo-power spectrum for a speech section and a second pseudo-power spectrum for a non-speech section detected in a voice signal output from a microphone array; and a speaker direction estimation section which detects a peak value in any one of the difference spectrum and the first pseudo-power spectrum, and estimates the direction of a speaker based on the direction angle corresponding to the detected peak value.

Observed signals x₁(t) to x_j(t) from a plurality of sensors disposed in two dimensions are subject to a short-time Fourier transform, from which signals X₁(ω₁) to X₁(ω_N), . . . X_J(ω₁) to X_J(ω_N) are derived. Using the independent component analysis process, separation matrices W(ω₁) to W(ω_N) are produced. Their inverse matrices H(ω₁) to H(ω_N) are calculated, and for each ω_n (n=1, . . . , N), using a pair of elements from each column of H(ω_n), H_ji(ω_n) and H_j′i(ω_n), an angle {circumflex over (θ)}_i,jj′(ω_n)=cos⁻¹(arg(|H_ji(ω_n)/H_j′i(ω_n))/(ω_nc⁻¹∥d_j−d_j′∥)) is calculated where arg(α) represents an argument of α, c the propagation velocity of a signal, and ∥d_j−d_j′∥ represents a spacing between sensors i and j′. Columns are permuted so that {circumflex over (θ)}_i,jj′(ω_n) obtained from each column of H(ω₁) to H(ω_N) assume an ascending order. For columns which cannot be permuted, the equation ∥q_i−d_j′∥/∥q_i−d_j∥=|H_ji(ω_n)/H_j′i(ω_n)|=DR_i,jj′(ω_n) is solved for q_i to calculate R_i,jj′(ω_n)=∥DR_i,jj′(ω_n)·(d_j−d_j′)/(DR²_i,jj′(ω_n)−1)|. Columns of H(ω_n) is permuted so that R_i,jj′(ω_n) assume an ascending order. H(ω_n) is used to solve the permutation problem of W(ω).

The present invention discloses a method, apparatus and computer program product for determining the location of a plurality of speech sources in an area of interest, comprising performing an algorithm on a signal issued by either one of said plurality of speech sources in the area to for iteratively recover data characteristic to said signal, wherein the algorithm is an iterative model-based sparse recovery algorithm, and wherein for each of a plurality of points in said area, the iteratively recovered data is indicative of a presence of a plurality of speech sources contributing to the signal received at each of a plurality of points in the area.

The present invention relates to a sound source separation and display method and a system thereof, and provides in particular a sound source separation and display method and a system thereof that are intended to eliminate a specific sound source. In order to separate a plurality of sound sources by using a single set of microphone array, the result of processing of sound source identification is utilized. More specifically, a signal in a direction is extracted from the result of the processing of the sound source identification, and a field limited to/eliminated of the effect of the signal is calculated and displayed. Such an operation can be repeated. A virtual reference signal can be created in a time domain.

An information presentation device includes an audio signal input unit configured to input an audio signal, an image signal input unit configured to input an image signal, an image display unit configured to display an image indicated by the image signal, a sound source localization unit configured to estimate direction information for each sound source based on the audio signal, a sound source separation unit configured to separate the audio signal to sound-source-classified audio signals for each sound source, an operation input unit configured to receive an operation input and generates coordinate designation information indicating a part of a region of the image, and a sound source selection unit configured to select a sound-source-classified audio signal of a sound source associated with a coordinate which is included in a region indicated by the coordinate designation information, and which corresponds to the direction information.

Method and apparatus for locating and quantifying sound sources using an array of acoustic vector probes (200). Signals received at the probes are converted to digital form and fed into a digital signal processor (400) which computes the sound pressure and the sound-intensity vector at each probe. The set of sound-intensity vectors measured by the array provides a set of directions to a sound source (100) whose approximate spatial coordinates are determined using a least-squares triangulation formula. The sound-intensity vectors also determine sound-power flow from the source. In addition sound pressure measured by the probes can be phased to form a sensitivity beam (250) for scanning a source. Sound-intensity measurements made concurrently can be used to determine the spatial coordinates of the part being scanned and the sound power radiated by that part. Results are displayed on a computer screen or other device (500) permitting an operator to interact with and control the apparatus. Additional related features and methods are disclosed.

Multiple Holocam Orbs observe a real-life environment and generate an artificial reality representation of the real-life environment. Depth image data is cleansed of error due to LED shadow by identifying the edge of a foreground object in an (near infrared light) intensity image, identifying an edge in a depth image, and taking the difference between the start of both edges. Depth data error due to parallax is identified noting when associated text data in a given pixel row that is progressing in a given row direction (left-to-right or right-to-left) reverses order. Sound sources are identified by comparing results of a blind audio source localization algorithm, with the spatial 3D model provided by the Holocam Orb. Sound sources that corresponding to identifying 3D objects are associated together. Additionally, types of data supported by a standard movie data container, such as an MPEG container, is expanding to incorporate free viewpoint data (FVD) model data. This is done by inserting FVD data of different individual 3D objects at different sample rates into a single video stream. Each 3D object is separately identified by a separately assigned ID.

Apparatus has means for computing correlation matrix of arrival wave, means for producing a matrix for obtaining eigenvalues and a matrix for obtaining eigenvectors, means for computing column norms on the matrix which is a subject for Householder transformation, means for judging whether a maximum value of the column norms is the same as or lower than a predetermined threshold, means for repeating Householder transformation on the matrix only if the maximum value of the column norms is judged not to be the same or not to be lower, means for breaking Householder transformation if the maximum value is the same as or lower, means for further implementing Householder transformation and for obtaining the eigenvectors and the eigenvalues of the correlation matrix, and means for judging the number of the arrival waves from the implementation times of Householder transformation.

The sound source tracking device of the present invention comprises a plurality of differential microphones having bidirectionality, and a support member adapted to support the plurality of differential microphones such that the plurality of differential microphones are disposed in an array within a given plane. The plurality of differential microphones are supported on the support member such that their principal axes of directionality are approximately orthogonal to the given plane.

The hybrid adaptive beamformer of the present invention includes a plurality of microphones for receiving sound energy from an external environment and for producing a plurality of microphone outputs from the sound energy. A processor produces a plurality of first order beams based on the microphone outputs. The processor determines an amount of reverberation in the external environment and adaptively produces a second order output beam taking into consideration the determined amount of reverberation. The processor may determine the amount of reverberation based on a comparison of the first order beams. The processor may produce the second order output beam by adaptively combining the plurality of first order beams, as further described below, or by adaptively combining the microphone outputs. The adaptation varies taking into consideration the determined amount of reverberation. Alternatively, the adaptation may vary by measuring the signal-to-noise ratio of the first order beams and adaptively combining the first order beams based on the determined signal-to-noise ratios.

A sound direction estimation apparatus includes a sound source model storage unit that stores likelihood of a sound source class in correspondence with a sound feature quantity, a sound signal input unit that receives a sound signal, a sound source identification unit that identifies a sound source class of the sound signal input from the sound signal input unit with reference to the sound source model storage unit based on the sound feature quantity of the sound signal, and a first sound source localization unit that estimates a sound direction of the sound signal of the sound source class identified by the sound source identification unit.