52 results about "Sound source identification" patented technology

The invention discloses a bridge expansion joint acoustic monitoring method based on a beam forming microphone array, and the method comprises the steps: synchronously collecting acoustic signals through multiple channels, and achieving the high-precision signal alignment through clock offset compensation; the noise analysis module is started by combining a pre-triggering signal of a vehicle passing through the detection unit, the optimal microphone distance is dynamically determined according to sound field coherence and a signal-to-noise ratio maximization model, and a stepping motor drives a movable microphone to achieve real-time adjustment. On the basis, a spatial filtering weight is generated by adopting a self-adaptive beam forming algorithm, and directional enhancement and interference suppression of the expansion joint sound source are realized. Through collaborative optimization of a hardware structure and a signal processing method, the sensitivity and accuracy of acoustic monitoring of the bridge expansion joint are effectively improved, the abnormal sound source recognition capability can be remarkably enhanced, the environmental noise interference is reduced, and the method is suitable for intelligent monitoring and diagnosis of the long-term service state of the bridge expansion joint.

The invention provides a marine acoustic environment monitoring system based on a noise map. The marine acoustic environment monitoring system comprises a buoy network module, a data fusion processing module, a noise map generation module, a noise source identification module and an edge response module, the buoy network module serves as a sensing tail end, multi-source information is gathered to the data fusion processing module through the first data bus for standardization processing, and then the noise map generation module is driven through the second data bus to construct a three-dimensional sound field model. The output of the model is transmitted to a noise source identification module through a third data bus for intelligent analysis and traceability; the alarm decision of the noise source identification module is output to the edge response module, and the edge response module dynamically regulates and controls the communication and acquisition strategy of each buoy node through a distributed control circuit to form a complete closed loop. According to the invention, high-precision and automatic monitoring of the marine acoustic environment is realized, the problems of limited monitoring range and single data dimension of the traditional means are solved, and the reliability and emergency capability of the system are improved.

The invention discloses a construction and recognition method for a sound source recognition network of vehicles in a highway tunnel, and the method comprises the steps: setting a distributed microphone array in the tunnel, and collecting a vehicle audio signal; a MobileNetV3 model based on the Mel frequency spectrum is constructed; the Mel spectrum feature extraction module is used for taking the Mel spectrum feature extracted by the Mel spectrum feature extraction module as input, taking a vehicle audio signal type as output, and training a MobileNetV3 model based on the Mel spectrum to obtain a sound recognition model; constructing a sound source localization model; according to the method, the MobileNetV3 model based on the Mel frequency spectrum is adopted for sound recognition, the sound source positioning model is constructed in a combined mode for sound source positioning, the MobileNetV3 model and the sound source positioning model work cooperatively, the feature description capability of abnormal sound of a vehicle is enhanced, the influence of tunnel echo and environment noise on recognition is effectively reduced, accidents in the tunnel are found in time and positioned accurately, and the method is suitable for popularization and application. The recognition and positioning accuracy of the vehicle sound source in the complex tunnel environment is improved, and the technical problem that in the prior art, the recognition precision of the accident sound in the tunnel is not high is solved.

The invention is applicable to the technical field of grouting of mining engineering and geotechnical engineering, and provides a grouting diffusion space real-time sensing and tracking method based on slurry dynamic sound source localization, which comprises the following steps: active enhancement and characteristic modulation of a slurry dynamic sound source; building a stereo wave sensing network and acquiring high-fidelity sound wave data; carrying out AI-driven slurry sound source identification and three-dimensional space positioning; and the grouting diffusion process is subjected to three-dimensional real-time visualization and intelligent early warning. The invention creatively provides a comprehensive solution integrating sound source active enhancement modulation, stereophonic sensing network, artificial intelligence deep noise reduction and three-dimensional space inversion positioning, and the core of the comprehensive solution lies in that slurry flow sound signals which are originally weak and difficult to use are actively enhanced and are endowed with features; a traceable slurry power sound source signal with a high signal-to-noise ratio is constructed, and three-dimensional space real-time sensing and visual tracking of the slurry diffusion frontal surface position and the overall diffusion range are realized in combination with an advanced signal processing and positioning algorithm.

The present application relates to the technical field of noise diagnosis, in particular to a method and device for identifying abnormal sound source applied to a vehicle transmission system, the method comprising: acquiring abnormal sound noise data, and confirming an abnormal sound generation period according to the abnormal sound noise data; acquiring transmission system vibration data, and performing time-frequency domain analysis on the transmission system vibration data and the abnormal sound noise data according to the abnormal sound generation period to obtain abnormal sound time domain data; the transmission system vibration data is obtained by vibration detection of the vehicle transmission system at a previous time; performing cross-correlation identification of the abnormal sound source according to the abnormal sound time domain data to obtain an abnormal sound source identification result; the abnormal sound source identification result is used to represent that the abnormal sound source is a gearbox and / or a drive axle. By considering the state of strong coupling connection between the gearbox and the drive axle in the vehicle transmission system, cross-correlation analysis is performed on the abnormal sound time domain data to identify the abnormal sound source, thereby improving the accuracy of identifying the abnormal sound source of the vehicle identification system.

This invention discloses an ultra-high resolution compressed spherical beamforming sound source identification method, comprising the following steps: 1) constructing a two-dimensional off-grid compressed spherical beamforming model in the spherical harmonic domain based on Taylor expansion; 2) providing prior assumptions for the unknown variables in the two-dimensional off-grid compressed spherical beamforming model in the spherical harmonic domain, and calculating the joint probability density function of the unknown variables; 3) solving for the unknown variables in the two-dimensional off-grid compressed spherical beamforming model in the spherical harmonic domain according to the joint probability density function of the unknown variables, and estimating the sound source DOA and sound source intensity. This invention overcomes the basis mismatch problem, accurately estimates the off-grid sound source DOA and quantizes the sound source intensity, and achieves ultra-high resolution.

The invention discloses a sound field calculation method fusing ridge estimation-truncated singular value method, and the method comprises the steps: 1) constructing an acoustic inverse problem solving model based on an inversion formula of sound source intensity Q; 2) inputting the measured sound pressure column vector PH into an acoustic inverse problem solving model, and solving the acoustic inverse problem solving model by using an RE-TSVD algorithm to obtain sound source intensity; and 3) predicting the sound pressure of any field point according to the sound source intensity and the transfer matrix from the equivalent source point to the target field point. According to the method, side lobe interference can be effectively suppressed, main lobe information of a larger sound field is reserved, and the method has better sound source recognition and positioning performance.

The present application belongs to the field of sound field reconstruction and sound source identification technology, and discloses an equivalent source sound field positioning method and system based on sparse sound intensity measurement, which acquires the complex sound pressure and complex particle velocity at the measurement point on the holographic surface through a vector sensor array, and calculates the measured sound intensity vector; constructs a sound intensity transfer matrix based on the equivalent source method, establishes a linear measurement equation between the measured sound intensity vector and the equivalent source intensity vector; by using the spatial sparsity of the equivalent source intensity, the solving of the equivalent source intensity vector is converted into an L1 norm sparse optimization problem, and a convex optimization algorithm is used to solve it to obtain the optimal equivalent source intensity vector; based on the optimal equivalent source intensity vector, the acoustic quantity at any point in the target sound field is reconstructed by combining the free field Green function. The present application uses the active component of the sound intensity vector to suppress non-related noise, improves the ill-conditioned inversion by combining the compressive sensing theory, and can realize high robustness and high precision sound source positioning and sound field reconstruction without reference microphones.

This invention discloses a construction area vibration zoning control method based on multi-source sound identification, relating to the field of engineering construction control. The method includes: acquiring the coordinate positions of the target construction area and the railway track, and determining the upper limit of vibration intensity in the target construction area; determining the target set of construction equipment, and based on the construction movement line, determining the fixed positions of the foundation construction equipment set within the target construction area to form a foundation equipment layout scheme, and generating a foundation construction vibration distribution through multi-source sound identification analysis; performing vibration zoning on the foundation construction vibration distribution, and based on the vibration zoning results and the upper limit of vibration intensity, optimizing the equipment layout of the movable construction equipment set based on the foundation equipment layout scheme to generate a target equipment layout scheme. This solves the technical problem in existing technologies where regional vibration load imbalance prevents the optimal allocation of vibration energy in the construction area, leading to both low construction efficiency and potential safety risks.

This invention relates to a method and system for locating coaxial counter-rotating sound sources in rotorcraft, belonging to the technical field of sound source identification methods. It includes: processing the sound pressure signal of the moving sound source using continuous wavelet transform to obtain time-frequency domain variation results; performing Doppler effect correction based on experimental conditions; estimating the spatial location and relative intensity of the noise source using a cross-spectral matrix and weight vector; and performing phase-locked analysis of the rotor noise source intensity using a phase-locked averaging method. This invention differs from traditional time-domain delay superposition algorithms and frequency-domain cross-correlation spectrum methods. It can image unsteady sound sources using time-frequency domain characteristics, solving the acoustic imaging problem of counter-rotating propeller noise and possessing potential applications in the rotorcraft industry.

This invention discloses an FPGA implementation method for visual image processing of sound source identification, comprising: 1. Normalizing the sound intensity values ​​obtained from sound source identification and converting them into grayscale values; 2. Obtaining corresponding color pixel values ​​by performing three independent mappings on the grayscale values; 3. Upsampling the low-resolution color acoustic cloud map using an interpolation algorithm to improve the image resolution and form a visual acoustic cloud map that represents sound intensity in color. This invention enables the visual image processing of sound source identification to be implemented on an FPGA in the form of digital circuits, thereby improving the real-time performance of the entire sound source identification process, reducing the hardware complexity of the sound source identification platform, and saving time and hardware costs.

The invention discloses a rotating sound source recognition and positioning method based on a deep convolutional attention neural network, and belongs to the field of rotating sound source recognition, and the method comprises the steps: firstly carrying out the motion compensation and modal expansion of a sound pressure cross-spectrum matrix through a modal decomposition rotating beam forming algorithm, and generating a sound source distribution diagram in a rotating scene; then the sound source distribution map is input into a deep convolution attention neural network model, the model adopts an encoder-decoder structure, a channel and a space attention mechanism are fused, key sound source features are dynamically focused, noise interference is suppressed, and high-precision reconstruction of the sound source distribution map is achieved; and finally, carrying out local maximum value detection on a prediction map output by the network, and extracting a sound source space position. According to the method, the Doppler effect and phase distortion problems in rotating sound source positioning are effectively overcome, and the positioning precision, the anti-noise performance and the calculation efficiency in a complex rotating field are remarkably improved.

The invention belongs to the technical field of acoustic measurement, and discloses a free space sound field measurement method and system, and the method comprises the steps: constructing a free light path sound field measurement system based on a Michelson interferometer principle; acquiring a reference image, introducing a sound field to be measured, and controlling a loudspeaker or a laser beam to perform translation and rotary scanning through a computer to complete interference pattern data acquisition; preprocessing the acquired interference pattern, identifying the edge of the interference fringes by using an edge detection technology, and carrying out light phase variation calculation; and performing sound field reconstruction by adopting a Radon inverse transformation algorithm, converting sound field integral information carried by a laser beam into a sound pressure value of each point in a real space, and reconstructing a free field sound pressure distribution diagram. The method has the advantages of no damage and low cost, the measurement precision of the sound field and the integrity and reliability of reconstruction are remarkably improved, the nano-scale optical path change caused by the sound field can be effectively captured, and the high precision of sound field measurement and the accuracy of sound source recognition are ensured.

The invention discloses a scenarized audio recognition method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence sound signal processing. The method comprises the following steps: inputting a collected original audio waveform into a shared feature extraction network for feature extraction processing to obtain a first audio feature map; performing prior information processing based on the first audio feature map and the external input vector to obtain dynamic sound source prior information; the dynamic sound source prior information comprises an internal activeness label vector and an external sound source activeness vector; performing parameter generation processing according to the internal activeness label vector and the external sound source activeness vector to obtain a feature modulation parameter; performing affine transformation processing on the first audio feature map according to the feature modulation parameter to obtain a modulated second audio feature map; and inputting the second audio feature map into a sound source classifier for identification processing to obtain a sound source identification result. The environment dynamic adaptability and accuracy of scenarized audio recognition can be improved.

This application discloses an integrated monitoring boundary marker system based on multimodal sensing and redundant transmission, relating to the field of nature reserve monitoring technology. The system includes multiple boundary markers, each containing sensing and imaging equipment, a dual-power management unit, a sound source identification unit, a multi-source data fusion processing unit, and a multi-mode communication unit electrically connected to the system. This system can effectively monitor and accurately transmit monitoring data for protected areas with an average area of ​​82,000 to 83,000 hectares when only 58 to 59 boundary markers are used. It offers high monitoring reliability and reduces data mistransmission.

This invention relates to a sound source identification method based on array expansion and SE (Search Engine Attention) mobile inverse bottleneck convolutional neural network, comprising: acquiring an 18-array sound source distribution map calculated using the MUSIC algorithm based on the sound pressure cross-spectrum matrix; inputting the 18-array sound source distribution map into an EAG-U-Net data conversion model to convert the 18-array sound source distribution map into a 64-array sound source distribution map, achieving the effect of expanding the microphone array. This model introduces the EAG mechanism to optimize model feature selection in the spatial and channel dimensions, improving the model's representational ability and accuracy in the data conversion process, and generating an acoustic imaging map after data conversion; inputting the 64-array sound source distribution map into a mobile inverse bottleneck convolutional neural network model based on the SE attention mechanism for feature extraction and image reconstruction to obtain a predicted sound source distribution map; and performing local maxima detection using the predicted sound source distribution map to obtain sound source localization and intensity.

Soundproof chamber construction equipment (100, 200, 300), comprising: an audio procurement unit (101, 201) that procures audio data that includes audio from a variety of sound sources; a sound source identification unit (102, 202) which, based on the audio data, determines a plurality of sound source positions as positions of the plurality of sound sources; an audio extraction unit (103) that generates a multitude of pieces of extraction audio data by extracting audio represented by the audio data with respect to each sound source and generating the extraction audio data representing the extracted audio; a format conversion unit (104) that generates a multitude of stereophonic tones corresponding to the multitude of sound sources by converting a format of the multitude of pieces of extraction audio data into a stereophonic audio format; a position procurement unit (105) that procures a listening position as a position at which audio is listened to; a motion processing unit (106) that calculates an angle and distance between the listening position and each of the multitude of sound source positions; an angle-distance adjustment unit (107) that adjusts each of the plurality of stereophonic tones using the angle and distance according to each of the plurality of sound source positions, thereby producing a plurality of set stereophonic tones as a plurality of stereophonic tones at the listening position; and a superposition unit (108, 308) that superimposes the multitude of set stereophonic tones.

The method of operation of a wearable device according to the present invention comprises: a step in which a microphone array module acquires noise magnitude information and direction information; a step in which a wireless communication module transmits the noise magnitude information and direction information to an external cloud server via a user's smart device under the control of a processor; a step in which the wireless communication module receives noise source identification information and risk level information corresponding thereto from the external cloud server via the smart device under the control of a processor; a step in which the processor generates a control signal to provide at least one stimulus among vibration stimulation, electric stimulation, and visual stimulation by a light-emitting element to a user using at least one of the noise magnitude information, the noise direction information, the noise source identification information, and the risk level information corresponding thereto; and a step in which at least one of a vibration element, an electric stimulation array, and a light-emitting element operates to generate at least one stimulus according to the control signal.

The embodiment of the invention provides an acoustic horn immersion type sound field rendering method and system, and relates to the technical field of sound field rendering, and the method comprises the steps: obtaining a user position and a head posture in a virtual space; acquiring the position and intensity of a virtual space sound source; identifying a focus sound source currently concerned by the user according to the user position, the head posture, the sound source position and the intensity; identifying sound sources except the focus sound source as environment sound sources; performing sound field rendering on the environmental sound source to obtain a shared environmental sound field signal, the computing resource of the environmental sound source being lower than the computing resource of the focus sound source; performing convolution operation on the original audio signal of the focus sound source and the head acoustic transmission function to obtain a personalized high-resolution signal; and superposing the shared environment sound field signal and the personalized high-resolution signal to generate a binaural signal, and playing the binaural signal through an acoustic horn array. According to the method, the immersion and the actual effect of virtual reality training can be improved.

The application discloses a kind of orthogonal matching pursuit sound source identification and positioning method based on feedback mechanism, measurement surface and focusing surface are set according to set distance interval, measurement surface and focusing surface are grid division, obtain microphone position and focusing point and Green function transfer matrix;The relationship between microphone measurement sound pressure vector and focusing point sound source intensity is obtained using Green function transfer matrix;The sound source intensity of focusing surface grid point is calculated by orthogonal matching pursuit algorithm based on source intensity priori guidance, and the obtained sound source intensity data is used for accurate identification and positioning of sound source.The application preliminarily selects atom by orthogonal matching pursuit algorithm based on source intensity prior information guidance, then the atom preliminarily selected is fed back to transfer matrix, and the atom closest to measurement sound pressure vector is obtained as the final selected atom, which improves the reconstruction performance of traditional algorithm at low frequency, and improves the sound source identification and positioning ability under the strong correlation between measurement matrix atoms.

The invention discloses a noise source identification test device for an axial flow compressor of a turboshaft engine, belongs to the technical field of aerodynamic noise analysis, and aims to solve the problems of poor real-time performance and low accuracy in noise identification of the axial flow compressor of the turboshaft engine. The axial flow compressor testing system is composed of a single-stage axial flow compressor model and a driving motor, the driving motor is connected with a compressor rotating shaft through a high-precision vibration reduction coupling, and a motor shell is covered with sound absorption cotton; the noise measurement system comprises a semicircular microphone array and a temperature acquisition unit, and a temperature sensor is mounted at a key position of a gas compressor; the noise source identification system comprises a high-speed acquisition module and a hybrid intelligent identification module; the acquisition module performs time domain filtering and spectral analysis preprocessing on original sound signals collected by a microphone, and the hybrid model realizes noise source real-time classification through multi-working-condition noise sample end-to-end training.

This application discloses a noise source identification method, apparatus, engineering equipment, and machine-readable storage medium, relating to the field of noise source identification technology. The method includes: acquiring near-field noise of a hoisting mechanism operating at different times; determining the vibration frequencies of various components of the hoisting mechanism at different times based on the drum rotation speed of the hoisting mechanism at different times; determining target times at each time based on abnormal frequencies in the near-field noise; determining target components based on all vibration frequencies and abnormal frequencies corresponding to the target times; and accurately identifying noise sources by locating the target components. When multiple target components exist, determining whether resonance exists between the target components based on the vibration frequencies of each target component at different drum rotation speeds; and generating noise source identification results based on the target components and resonance information when resonance exists. This provides a basis for optimizing the vibration and noise characteristics of the hoisting mechanism.

The invention discloses a method for positioning a pulse sound source under a strong harmonic background, which comprises the following steps of: arranging a microphone array in a sound field containing a pulse sound source to be detected to acquire a time domain sound pressure signal, performing continuous wavelet transform on the time domain sound pressure signal to obtain a wavelet coefficient matrix, and determining a characteristic frequency band with remarkable pulse signal energy based on a time frequency distribution diagram; performing inverse continuous wavelet transform on the wavelet coefficient matrix in the characteristic frequency band to obtain a pulse signal enhanced reconstructed wavelet time domain signal; and taking the reconstructed wavelet time-domain signal as input, reconstructing wavelet time-domain sound pressure on a sound source reconstruction surface by adopting a time-domain equivalent source method, identifying a pulse generation target moment according to the reconstructed sound pressure, generating a sound pressure cloud picture, and determining the position of a pulse sound source through the maximum sound pressure amplitude, thereby realizing positioning of the pulse sound source under the strong harmonic background. According to the method, the precision and reliability of pulse sound source positioning are remarkably improved, and an effective technical means is provided for transient sound source recognition and acoustic imaging in a complex sound field.

The invention discloses an environment easy-to-mix sound identification method and system based on a double-layer model and a storage medium. The method comprises the following steps: converting an audio time sequence into a spectrogram through Fourier transform with a time window; image features in the spectrogram are extracted through an artificial intelligence model, the type of a sound source is recognized, if the type of the sound source is a sound source not prone to confusion, a sound source recognition result is directly output, and if the type of the sound source is a sound source prone to confusion, the corresponding spectrogram is input into a recognition model of the sound source prone to confusion to be recognized, the easy-to-confuse sound source identification model is different from the artificial intelligence model in that only the easy-to-confuse sound source is used for training and identification. According to the method, the accuracy of current environment sound source recognition is improved.

The application belongs to the technical field of acoustic measurement, and discloses a free space sound field measurement method and system, which constructs a free light path sound field measurement system based on the principle of Michelson interferometer; collects a reference image, introduces a sound field to be measured, performs translation and rotation scanning through a computer to control a loudspeaker or a laser beam, and completes interference pattern data collection; pre-processes the collected interference pattern, identifies the edge of the interference fringe by using edge detection technology, and calculates the light phase change amount; sound field reconstruction is performed by using a Radon inverse transform algorithm, sound field integral information carried by the laser beam is converted into the sound pressure value of each point in the real space, and a free field sound pressure distribution map is reconstructed. The application has the advantages of non-destructive and low cost, and significantly improves the sound field measurement accuracy and the integrity and reliability of reconstruction, can effectively capture the nanometer level optical path change caused by the sound field, and ensures the high accuracy of sound field measurement and the accuracy of sound source identification.

This invention provides a method, apparatus, and storage medium for multi-source sound recognition based on depthwise separable convolution, relating to the field of audio signal processing technology. The method acquires raw audio data containing multiple sound source signals and environmental noise within a target monitoring area using a microphone array. This data is then standardized to obtain standardized time-domain audio data. Feature transformation and filtering are performed on this data to obtain characteristic audio data. A lightweight convolutional backbone network, trained on-the-fly, extracts high-dimensional features layer by layer. A hybrid pooling strategy, combining global average pooling and global max pooling, is then used to aggregate global features, resulting in aggregated feature vectors. Finally, classification and coordinate regression processing are performed to output sound source classification results and three-dimensional spatial coordinate data. This invention achieves integrated multi-source sound recognition and localization in complex environments with lightweight deployment, adapting to diverse application scenarios.

The invention relates to the technical field of machine learning, in particular to a sound source recognition method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a target sound source; performing sound source analysis on the target sound source to obtain a target sound source positioning feature of the target sound source, a target noise feature of the target sound source and a target transmission physical feature of the target sound source; performing acoustic feature extraction on the target sound source based on an acoustic feature extraction layer of a target sound source recognition model to obtain a target acoustic feature of the target sound source; and inputting the target sound source positioning feature, the target noise feature, the target transmission physical feature and the target acoustic feature into a sound source classification layer of the target sound source recognition model for classification and recognition to obtain a classification and recognition result of the target sound source. The scene adaptability and accuracy of sound source recognition in different environments can be improved.

The invention provides portable noise pollution source rapid identification equipment, which comprises a folding bracket, a dustproof box body, a noise source reading assembly, a noise isolation orientation device, a sound source identification assembly and an environment monitoring assembly, and is characterized in that the dustproof box body is fixedly mounted above the folding bracket; the noise source reading assembly is installed in the dustproof box body, the noise isolation orientation device is arranged on the outer ring of the noise source reading assembly in a sleeving mode, and the sound source recognition assembly and the environment monitoring assembly are arranged in the dustproof box body and electrically connected with the noise source reading assembly. The environment monitoring assembly is used for monitoring environment data of the equipment, the environment data comprises temperature, humidity, wind speed and air pressure data, the dustproof box body is in a hollow cylindrical shape, and the dustproof box body comprises a top cover, a bottom cover and a side dustproof net. According to the invention, automatic identification and positioning of the noise source are realized, and the noise source identification accuracy is effectively improved.

The invention relates to the technical field of fault detection, and discloses a thermal runaway detection method, device and equipment, a storage medium and a program product. According to the method, initial delay summation output corresponding to a plurality of grid points is obtained through a multiple signal classification algorithm to serve as an iteration initial value, a plurality of sound source positions can be effectively positioned, a sound source recognition result is sharpened through multiple iterations, the main lobe width is reduced, side lobe interference is attenuated, the detection resolution is improved, and the detection accuracy is improved. The initial delay summation output corresponding to the multiple grid points is screened through the preset screening condition, the initial delay summation output which does not meet the preset screening condition is replaced with the preset value to directly serve as the output result, the output result does not participate in the subsequent iteration process, the calculation amount can be reduced, the detection efficiency can be improved, and therefore the thermal runaway detection accuracy can be improved when the thermal runaway detection function is achieved. And the resolution ratio and the efficiency are high.

The invention discloses an active coherent plane wave silencing device and a sound source identification method. The device comprises a sound wave guide tube designed according to the highest frequency of a target sound wave, a main sound source sound pressure signal acquisition module, a signal analysis processing control unit, an adjustable power amplifier and a secondary sound source. The sound wave guide tube converts spherical waves or cylindrical waves into plane waves through a cut-off section or an adjustable section structure; the signal analysis processing control unit identifies the characteristic parameters of the main sound source through time-frequency transformation and phase inversion calculation and generates a control signal; the secondary sound source generates interference sound waves with the same frequency and opposite phases as the main sound source, and the two form surface contact interference in the conduit to counteract target sound energy. Plane wave interference is achieved through the sound wave guide pipe, real-time sound pressure monitoring and a self-adaptive algorithm are combined, sound wave parameters are dynamically adjusted, wide-band and multi-sound-source type self-adaptive noise elimination can be achieved, and sound energy counteracting efficiency and system stability are remarkably improved.