390 results about "Sound source location" patented technology

The invention discloses a building sound wave reflection path analysis and noise source positioning method and system, and the method comprises the steps: S1, arranging a plurality of sound sensors according to the geometric structure of a target building, collecting sound signals, carrying out the signal sampling of the sound signals, and storing the sound signals in the form of a time sequence; s2, preprocessing the sound signal, extracting a time difference matrix, a frequency spectrum feature and an energy feature, merging into a three-dimensional time sequence feature, and normalizing to form a three-dimensional time sequence feature matrix; s3, establishing an LSTM network, and predicting a three-dimensional position and a sound wave reflection path of an output noise source; s4, optimizing and calculating a sound wave reflection path by using the geometric structure data of the target building, and generating a three-dimensional reflection path diagram; and S5, dynamically displaying a noise source position and a sound wave reflection path according to the three-dimensional reflection path diagram. According to the invention, high-precision three-dimensional positioning of a noise source can be realized in a complex building environment, accurate modeling is realized, and propagation and reflection paths of sound waves in a building are analyzed.

The invention discloses an array type multichannel acoustic imaging device and an abnormal sound source positioning display method, and the device comprises a main microphone array which is used for collecting sound signals of a target region; the camera device is used for collecting an image of the target area; the thermal imaging device is used for collecting a thermal image of the target area; the first data processing and analyzing module is used for identifying an abnormal sound signal of the target area and preliminarily estimating the position of an abnormal sound source; acquiring sound field distribution of the abnormal sound source and generating a sound field image by using an acoustic imaging algorithm; the second data processing and analyzing module is used for inputting the change rate of the sound wave parameters of the unit area and the temperature into a second neural network model, obtaining the sound velocity of the unit area in the sound field image, calculating the temperature of the unit area, fusing the sound velocity of the unit area with the temperature of the corresponding unit area in thermal imaging and determining the abnormal temperature; and the display screen is used for displaying the sound field image marked with the abnormal temperature. The abnormal sound source can be effectively distinguished and positioned, and the abnormal temperature can be displayed.

The invention relates to a sound source positioning method based on a double-microphone array, and belongs to the technical field of acoustic positioning. The method comprises the steps that sound source signals received when two microphones rotate at a constant speed along with a rotating disc are collected every a period of fixed time, and the collected multiple sound source signals with time delay are equivalently simulated into a circular array composed of the multiple microphones; converting the time domain signal into a frequency domain signal, calculating a cross-correlation function between different frequency domain signals, and weighting and filtering the cross-correlation function; inverse Fourier transform is carried out on the processed cross-correlation function, so that frequency domain information is converted into a time domain, and cross-correlation functions among virtual microphone positions at different time points are obtained; in a unit space for grid division, the position of a sound source is found by adopting a strategy of first rough search and then fine search. According to the method, the use number of microphones can be reduced, the hardware cost is reduced, meanwhile, the calculated amount can be greatly reduced, and the real-time performance of sound source positioning is improved.

The invention discloses a campus security management and control method and system fusing multiple systems, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting a video stream and an audio signal; adopting a human body posture estimation model based on an attention mechanism to obtain human body posture information, calculating abnormal behavior data, and designing an abnormal event judgment function for judging whether an abnormal event occurs or not; the method comprises the following steps: constructing a campus security knowledge graph, constructing a graph neural network model, and when an abnormal event occurs, learning embedded representation of an abnormal event entity by using the graph neural network model, and analyzing a reasoning reason of the abnormal event; a sensor combination is obtained based on the abnormal audio signals, the signal similarity of each sensor pair in the sensor combination is calculated, and a candidate sound source position with the highest matching degree score is obtained through calculation according to the signal similarity and serves as the occurrence position of an abnormal event; sending the occurrence position of the abnormal event and the reasoning reason of the abnormal event to campus security personnel; and campus personnel safety monitoring based on multi-system data is realized.

The invention relates to the technical field of arc fault diagnosis, and provides an energy storage system arc fault diagnosis method and system based on voiceprint detection, and the method comprises the following steps: collecting a voiceprint signal in real time, and carrying out the anti-interference preprocessing, and obtaining a time-frequency domain spectrogram; identifying the time-frequency domain spectrogram based on a multi-branch convolutional neural network and weighting the time-frequency domain spectrogram to obtain a time sequence feature sequence; inputting the time sequence feature sequence into a time sequence convolutional network model, and outputting global context features; performing dynamic weighted pooling on the global context features to obtain key frame feature vectors representing the arc fault; and inputting the key frame feature vector into an arc fault diagnosis classification model to obtain an arc fault type, an arc fault severity grade and sound source positioning information, and generating a corresponding alarm signal and a control instruction based on the arc fault severity grade. According to the invention, the detection sensitivity and the anti-interference capability are improved, and the safety protection level and the fault early warning capability of the energy storage system are improved.

The invention relates to the field of gas leakage identification, and discloses a gas leakage identification method and system based on sound localization, a medium and equipment, and the method comprises the steps: collecting a leakage sound wave signal through a microphone array, and extracting acoustic features in a complex background; simulating a diffusion process of gas in a turbulence environment after gas leakage through an established leakage gas convection-diffusion model, and combining gas concentration distribution in the simulated diffusion process with acoustic characteristics to predict sound source localization at a leakage position; modeling a sound source localization search process as POMDP, and iteratively updating a confidence state of a sound source position through a particle filter; spatial distribution features are extracted from the confidence state through DBSCAN clustering to serve as input of the LSTM-DQN network, spatial and temporal features are fused through the constructed LSTM-DQN network, cross-scene generalization is achieved through transfer learning, and sound source coordinates are dynamically optimized and output through the Q network. According to the invention, the positioning accuracy and real-time performance in a complex environment are improved.

The invention belongs to the technical field of sound source processing, and provides a sound source positioning and detecting method and device. The method comprises the following steps: acquiring delay estimation of microphones I and II and delay estimation of microphones I and III based on a three-path linear uniform microphone array; based on the delay estimation sum, respectively carrying out positioning calculation under near-field and far-field conditions; according to the sound source distance under the near-field condition, comparing the sound source distance with a distance judgment threshold value, and determining a far-field / near-field output sound source position; and carrying out feature extraction on the signals of any microphone array, and carrying out event classification based on a pre-trained convolutional neural network. According to the method, far / near field model selection is carried out according to the distance judgment threshold, large deviation generated by a single model in a critical region is avoided, continuous and stable positioning from short distance to long distance is achieved, time classification can be achieved while position calculation is carried out, and integrated output is achieved.

The invention discloses a lithium battery thermal runaway sound source positioning error compensation method based on XGBoost, and the method comprises the steps: collecting sound signals, including time delay and cross-power spectrum data, in an energy storage cabin in a fixed microphone array, and sound source positioning coordinates calculated by a conventional geometric method, and then comparing the geometric positioning coordinates with actual sound source position coordinates, thereby achieving the compensation of the thermal runaway sound source positioning error of a lithium battery. And calculating a positioning error, and constructing a feature vector. And training an XGboost model by using the feature vectors. In practical application, time delay, cross-power spectrum data and geometric positioning coordinates which are calculated in real time are input, traditional geometric sound source positioning coordinates are corrected through error values obtained through the prediction model, and therefore the more accurate sound source position is obtained. According to the method, an XGBoost error compensation model is introduced to correct errors generated by geometric positioning, so that the position of the thermal runaway lithium battery is accurately positioned.

The invention relates to the technical field of data processing, in particular to a three-dimensional live-action model construction system for building structure demolition and reconstruction. The system extracts event combinations with obvious transfer correlation features for localization and analysis of dangerous sound sources. Through simulation of transmission of test waves in an initial acoustic slowness field, a most probable dangerous sound source position for generating a current event combination under each test wave is obtained by using an objective function optimization solution method. And further performing statistics on simulation information of all test waves, establishing a residual curve, screening out a brittleness event position based on feature extraction, and adjusting a slowness value in a current voxel through various parameters corresponding to the brittleness event position in an initial acoustic slowness field. Through the dynamic correction method, the obtained corrected acoustic slowness field can more effectively represent the specific change of the building structure in the dismantling process, so that the internal damage can be more clearly quantified.

The invention discloses a residual network sound source localization method and system based on a fusion attention mechanism, and the method comprises the steps: adding a 1 * 1 convolution kernel, adding an extra SEAM module to form an SC-SEAM module, adding the SC-SEAM module in the last two stages of a residual network structure, forming a residual network architecture of a fusion channel attention mechanism module, and carrying out the sound source localization of the residual network. An indoor sound source positioning model is trained and generated, and the method comprises the following steps: after a microphone array obtains a to-be-positioned sound signal, STFT is carried out on the to-be-positioned sound signal received by each microphone; extracting a phase component between the STFT coefficients of the sound signals to be positioned received by each microphone, and obtaining a difference to obtain a phase difference diagram between each microphone; and inputting the phase difference image into an indoor sound source positioning model, and outputting an estimated angle value of the sound signal to be positioned. The method is high in sound source positioning accuracy and strong in robustness in indoor reverberation, noise and other multi-scene complex environments.

The embodiment of the invention provides an audio processing method and electronic equipment. The method comprises the steps that firstly, a source audio signal, a corresponding sound source position of the source audio signal in a virtual scene and a corresponding first position of a user in the virtual scene are acquired, and the virtual scene is obtained by modeling a real scene; according to the sound source position and the first position, obtaining a first acoustic feature, the first acoustic feature being obtained by adjusting a second acoustic feature according to the conversion information, and the second acoustic feature being obtained by performing acoustic feature extraction according to the first position, the virtual scene and the sound source position, the conversion information is used for describing a conversion relationship between acoustic features of the same position in the virtual scene and the real scene; a spatial audio signal is then generated from the first acoustic feature and the source audio signal. In this way, the user can experience the spatial audio signal with high quality and strong sense of immediacy.

The invention discloses a method and a device for realizing sound source localization by using a microphone array carried by an unmanned aerial vehicle. The method comprises the following steps: S1, acquiring a multi-channel audio signal which is measured by the microphone array carried by the unmanned aerial vehicle and contains flight noise of the unmanned aerial vehicle; s2, extracting a beam forming diagram of the multi-channel audio signal by using a conventional beam forming technology; and S3, inputting the beam forming diagram into the trained neural network model to obtain a predicted sound source position and sound source intensity. The invention aims to overcome harsh sound array conditions (limited array aperture and relatively small array element spacing) and complex acoustic environment (extremely low signal-to-noise ratio and unmanned aerial vehicle self-noise non-stationary characteristics) on the unmanned aerial vehicle, and realizes efficient and real-time sound source localization of a high-frequency sound source by the microphone array carried by the unmanned aerial vehicle.

The invention discloses a conference sound reinforcement system based on an AI intelligent algorithm and 360-degree omni-directional noise reduction, and relates to the technical field of audio signal processing, the conference sound reinforcement system comprises a conference management center, the conference management center is in communication connection with the following modules: a multi-sound-source sensing module used for capturing 360-degree omni-directional sound field information in a conference environment and constructing a sound source space distribution model; according to the invention, the omnidirectional microphone array unit covers all directions of a conference space, synchronously collects audio data streams, eliminates the limitation of a conventional unidirectional microphone, combines the sound source positioning space mapping unit, constructs a sound source space distribution model based on the time difference of arrival and the phase difference through a deep learning model, and improves the sound source positioning accuracy. The azimuth angle, pitch angle and distance parameters of the sound source are accurately analyzed, the position of the sound source is mapped to a virtual space coordinate system, a dynamically updated 3D sound source distribution diagram is generated, the position change and intensity distribution of the sound source are reflected in real time, and the positioning precision in a complex acoustic environment is remarkably improved.

The present invention discloses an online monitoring method for offshore wind turbine blades based on voiceprint recognition, which belongs to the field of acoustic signal analysis of wind turbine blades. The present invention obtains wind turbine voiceprint data, weather condition data and wind turbine power generation data, constructs a signal processing model, denoises, amplifies and extracts features of the collected voiceprint signal through a signal amplifier, a filter and a feature extraction unit, constructs a deep learning model, imports voiceprint signal parameters, weather data and wind turbine power generation into the deep learning model to evaluate future wind turbine blade cracks, constructs a crack assessment model, imports crack sound source position, crack depth, crack length and estimated damage score into the crack assessment model to evaluate the degree of wind turbine blade cracks under future weather conditions, classifies wind turbine cracks according to the crack damage score and gives an alarm, and realizes early warning of future wind turbine blade cracks by combining the voiceprint signal, weather condition and power generation of offshore wind turbine blades.

The invention discloses an indoor multi-sound-source positioning method based on DOA estimation and DOA association, and the method comprises the steps: judging single-source time-frequency points through the correlation of time delay inequality vectors of adjacent time-frequency points between microphones, and constructing a variable-size elastic single-source region; applying the FSSZ distribution condition of the sound source in a reference array to all arrays by using the high correlation characteristic of the FSSZ time-frequency distribution of the same sound source at different microphone arrays in a frame; performing middle DOA estimation on the FSSZs of all the arrays in the whole observation time period by using a circular integral cross spectrum method, and constructing a sound source position histogram in combination with FSSZ aggregation degree weighting; 2D positions of the plurality of sound sources are estimated from the sound source position histogram using 2D-CFAR. According to the invention, under the conditions of indoor reverberation and noise, position estimation can be carried out on a plurality of sound sources with an unknown number, the detection performance under the conditions of positioning precision and missed detection is obviously superior to that of an existing method, and the defects of the prior art are overcome.

The present invention discloses a cross-sound zone sound source localization method using dual hydrophones in a deep-sea environment, comprising: step 1: using the dual hydrophones to receive broadband signals radiated by a sound source in a specified sea area, and simultaneously using other hydroacoustic equipment to obtain environmental parameters of the sea area; step 2: using a time-spectral energy accumulation method to extract the arrival times of different sound lines from the received signal of one of the hydrophones, and estimating two multipath time delay differences; step 3: using a sound field calculation model BELLHOP to simulate two multipath time delay differences in different sound zones when the sound source is at different grid virtual points; step 4: calculating a cost function, and estimating the position of a target sound source; step 5: repeating steps 2 and 4 for the signal received by another hydrophone, and estimating the position of the target sound source; and step 6: performing a similarity analysis on four estimation results obtained by positioning the two hydrophones, and taking the average of the two estimation results with the greatest similarity as the final sound source position estimation result.

The invention provides a method and device for carrying out noise detection on a train, which can be applied to the field of railway and urban rail transit, and the method comprises the following steps: determining the initial noise reverberation duration and the sound source position of noise; iteratively executing the following operations to obtain the dynamic reverberation duration: determining the nth reverberation duration according to the sum of the (n-1) th noise reverberation duration and the preset duration; processing the nth noise reverberation duration and the reference reverberation duration according to tunnel shape parameters for the pipe tunnel to obtain an nth room coefficient; processing the nth room coefficient by using a target noise detection model corresponding to the sound source position to obtain an nth sound pressure level increment value of the noise; under the condition that a difference value between the Nth sound pressure level increment value and a preset standard sound pressure level increment value meets a preset convergence condition, determining the Nth noise reverberation duration as a dynamic reverberation duration; and processing the reference reverberation duration and the dynamic reverberation duration according to a reverberation noise prediction algorithm to obtain a noise value of the noise.

The invention belongs to the technical field of computer hearing, and discloses an audiovisual binaural sound source localization method based on a pulse neural network. Constructing an audio-visual sound source positioning network; frequency domain features and time domain features of the binaural audio signals are extracted through the frequency domain feature extraction module and the time domain feature extraction module respectively; spatial geometric features of the depth image are extracted through a spatial cue extraction module; the multi-source feature adaptive fusion module effectively fuses the frequency domain features, the time domain features and the spatial geometric features; the positioning module predicts the azimuth angle and the distance of the sound source based on the fusion features; and sound source prediction training is carried out by using the data in the front and back directions, and the trained audio-visual sound source positioning network is used for audio-visual binaural sound source positioning. The method improves the precision and robustness of sound source localization in a complex scene, and opens up a new path for the application of the multi-modal fusion technology in sound source localization. The difference between the front and rear sound sources is explicitly learned, so that the model learns a more complex nonlinear relationship between audio-visual characteristics and sound source positions.

The invention relates to a sound source positioning method based on a microphone array. Comprising the following steps: arranging a plurality of microphones to form an array, and collecting sound source signals; preprocessing the collected signals, wherein the preprocessing comprises filtering, denoising and gain control; calculating the time difference (TDOA) of the signals received by the microphones, obtaining the time difference through cross correlation operation, and carrying out preliminary positioning through a geometric positioning algorithm; a plurality of signal processing algorithms, such as a least square method, a maximum likelihood estimation method and a high-resolution spectrum estimation method, are applied to optimize the preliminary positioning result; designing an adaptive filter, dynamically adjusting filter parameters according to environmental noise and interference changes, and ensuring high-precision positioning in different environments; and outputting the final sound source position, and performing visual display.

The invention relates to the technical field of ultra-short baseline positioning, in particular to an ultra-short baseline positioning method and system based on a TDOA observation value and an effective sound velocity, and the method comprises the steps: constructing a spatial geometric distance mathematical relationship between a hydrophone and a sound source target according to a position vector of the hydrophone in a USBL system in a carrier coordinate system, and building an ultra-short baseline TDOA observation equation; unknown parameters of the ultra-short baseline TDOA observation equation comprise an effective sound velocity parameter and a sound source position vector parameter; substituting the effective sound velocity approximate value and the sound source position vector approximate value into an ultra-short baseline TDOA observation equation to linearize the ultra-short baseline TDOA observation equation; and iteratively solving the linearized ultra-short baseline TDOA observation equation by using a least square method criterion, and obtaining a final effective sound velocity and a final sound source target position vector according to a solving result. The method can be suitable for a USBL system with TDOA as an original observation value, can avoid the influence of sound ray bending, is simple in calculation principle, and can be embedded into a novel USBL positioning system for use.

The invention discloses a fan fault detection and positioning method and system based on a microphone array, and the method comprises the steps: obtaining a sound signal, and carrying out the first preprocessing of the sound signal, and obtaining a first sound signal; obtaining the position of a fault sound source by calculating the time difference of the first sound signals received by the microphones; optimizing the position of the fault sound source by using a first optimization method to obtain a position coordinate of the fault sound source; classifying fault types based on the fault sound source position coordinates to obtain a classification result; acquiring real-time monitoring data, and performing modeling in combination with the classification result to obtain a first neural network model; according to the method, historical operation data and historical fault information are acquired, the first neural network model is trained, the trained first neural network model is verified, a parameter prediction value is output, the parameter prediction value is judged, a fault result is obtained, and the deployment and extension cost is reduced through modular design and software and hardware optimization.

The invention discloses a sound signal acquisition method, a microphone array system and a storage medium, and relates to the technical field of microphone arrays, and the method comprises the steps: collecting sound data through a microphone array, and collecting environment point cloud data through a millimeter wave radar; performing joint calibration on the microphone array and the millimeter-wave radar, and determining a conversion matrix between the microphone array and the millimeter-wave radar; identifying and marking the sound data to obtain truth value data; projecting the environment point cloud data through the conversion matrix to obtain a corresponding relation between the environment point cloud data and the truth value data; the maximum weight matching algorithm is adopted to match the truth value data and the environment point cloud data with the corresponding relation, the sound source position is determined according to the obtained matching result, and the microphone array is controlled to collect sound signals based on the sound source position, so that the accuracy of the determined sound source position is improved. And the pointing adjustment accuracy of the microphone array is improved.

The invention discloses a method and system for ensuring smooth communication in a large private car, and relates to the technical field of vehicle-mounted central control. Comprising the steps of collecting audio data in a vehicle and extracting reference sound data of central control equipment; performing denoising processing on the audio data to generate communication voice data; separating the alternating current voice data according to the significant sound source; calculating an output sound pressure level of each loudspeaker according to a microphone position corresponding to the separated alternating current voice data, and performing voice gain compensation; accurate selection of communication voice data among passengers is realized by removing reference voice data in the audio data; accurate matching between passenger sound and seats is realized through accurate extraction of a significant sound source, and the position of the sound source is updated in real time; through the cooperation of the output sound pressure level and the voice gain compensation, the audio output is optimized, echoes caused by sound amplification of a loudspeaker are avoided, and the communication experience of passengers is improved.

The invention relates to a voice control method and device, equipment, a storage medium and a vehicle, and the method comprises the steps: obtaining a sound source positioning result in a target sound region, and enabling the target sound region to be a sound region where a voice instruction is detected at a target moment; obtaining an in-vehicle image collected at a target moment; performing image recognition processing on the in-vehicle image to obtain personnel position information of personnel in the vehicle, and determining a target position closest to the sound source based on the personnel position information of the personnel in the vehicle and the sound source positioning result; and controlling a target device corresponding to the target position to execute an operation corresponding to the voice instruction. According to the method, the sound source position of the voice instruction is determined, the position of the person in the vehicle corresponding to the sound source position is determined in combination with the image in the vehicle, the target device controlled by the voice instruction target is determined, the user sending the voice instruction can be accurately positioned, the device in the vehicle which the user wants to control is determined, the voice interaction accuracy is improved, and the user experience is improved.

The invention provides a livestock and poultry abnormal sound recognition and positioning method and system, and relates to the technical field of livestock and poultry breeding monitoring, and the method comprises the steps: extracting the sound characteristics of a livestock and poultry sound signal, carrying out the abnormal audio recognition according to the characteristic pattern of the sound characteristics, and obtaining the sound type of the sound signal; when the sound category is abnormal audio, calculating a generalized cross-correlation delay inequality to determine a sound source position range of the sound signal; and calling a particle swarm optimization algorithm to search the generalized cross-correlation delay inequality to obtain a distance value of an optimal sound source position of the sound signal, calling an SRP-PHAT algorithm to search to obtain a sound source position space point of the sound signal in a sound source position range, and finally determining a three-dimensional position coordinate when the livestock and poultry make a sound. Through the method and the device, the defects that livestock abnormal audio recognition is easily interfered by factors such as external environment noise and the like in the prior art, and the sound source positioning precision is limited in a multi-sound-source scene are overcome.

The embodiment of the invention provides an audio control method and device, a medium and equipment, and the method comprises the steps: determining the target position information of a sound production part corresponding to a sound production action when detecting that a target object exists in a current playing image and the target object executes the sound production action; according to the target position information, generating a target audio output parameter required by the audio output device to output the audio; and based on the target audio output parameter, controlling audio output equipment to output audio corresponding to the target position information. According to the audio control method provided by the embodiment of the invention, the target audio output parameter required by the audio output equipment is generated according to the target position information of the sound production part corresponding to the sound production action when the target object is detected to exist in the current playing picture and the target object executes the sound production action; the audio output device is controlled to output the audio corresponding to the target position information, the sound source position and the picture action are synchronized, and the audio-visual feeling of a user is improved.

The invention relates to an equipment health optimal monitoring point selection method and system, and relates to the field of equipment monitoring, and the method comprises the steps: collecting sound signals of monitoring points around monitored equipment; determining a signal spectrum and a signal attenuation based on the sound signal; determining a sound source position according to the signal spectrum and the signal attenuation; calculating a signal-to-noise ratio based on the signal spectrum, matching and determining a noise reduction level, and weighting according to the noise reduction level and the sound source position to obtain a comprehensive score; calculating a comprehensive score in combination with the sound source position and the noise reduction level; according to the monitoring points and the sound signals, calculating a fluctuation ratio, and screening out a position with the fluctuation ratio smaller than a stable threshold value and higher than a score threshold value in the comprehensive score as a secondary monitoring point; determining the position distribution of secondary monitoring points according to the sound source position and the equipment structure; and selecting a point location with the maximum coverage degree from the position distribution, and determining the point location with the highest coincidence degree as the optimal point location based on matching of the signal spectrum and the fault spectrum. The application has the effects of improving the monitoring effect and realizing high-precision monitoring.

The invention relates to a method for obtaining the position of a sound source relative to a dedicated reference point. A first sound signal and a plurality of second sound signals synchronized in time are recorded. The position may be obtained by applying the estimated filter to a correlation signal obtained by correlating the first sound signal with at least one of the plurality of second sound signals in the frequency domain. Two timing values are derived in the at least one filtered correlation signal, the two timing values exceeding a dedicated threshold in the time domain. The distance between the dedicated reference point and the sound source is then calculated based on the first timing value and the second timing value that are each obtained.

The invention discloses a multi-voice separation method based on a lightweight dual-path Transform network, and the method comprises the steps: collecting audio multi-voice data, and carrying out the preprocessing of the data, and forming a data set; the method comprises the following steps: constructing a dual-path Transform network model DPTNet, and introducing a recurrent neural network to optimize the dual-path Transform network model DPTNet; and training the dual-path network model DPTNet, and performing engineering deployment based on the trained model. The method is beneficial to obtaining higher-quality audio fingerprint recognition capability, sound source separation capability and voice enhancement function, can be used for tracking and positioning the position of a sound source, helps positioning and tracking related applications, can be expanded to the medical field, can be used for heart sound segmentation, namely, recognition of specific signals of the heart, and can be applied to the field of medical science. The method helps to diagnose cardiovascular and other medical problems, and has technical innovation and practical application value.

An audio apparatus comprises a first receiver (501) receiving audio data for audio sources of a scene comprising multiple rooms. A determiner (509) determines a room comprising a listening position and a neighbor room. A second receiver (503) receives spatial acoustic transmission data describing a number of transmission boundary regions for the listening room having an acoustic transmission level of sound from the neighbor room to the listening room exceeding a threshold. A first reverberator (511) determines a reverberation audio signal for the neighbor room. A sound source circuit (513) determines a sound source position in the neighbor room for a transmission boundary region. A renderer (507) renders an audio signal for the listening position which includes an audio component generated by rendering the second room reverberation audio signal from the sound source position. An improved rendering of multi-room scenes can be achieved.