Humanoid robot sound perception positioning method

Abstract

This application provides a sound perception and localization method for humanoid robots, relating to the field of robotics. It constructs a basic three-dimensional sampling array by scientifically arranging four microphones, employing high-precision omnidirectional condenser microphones to achieve distortion-free acquisition of environmental sound signals. Simultaneously, it outputs raw sound signal data, precise sound pressure amplitude data, and related identification information. The raw sound signal is preprocessed using A-weighted sound levels that conform to human hearing characteristics, while filtering out DC components and high-frequency interference noise to output a human-like sound signal. Then, based on the theory of pulsating spherical source radiation sound field and a point source approximation model, combined with the human-like sound pressure data acquired by the microphone array and preset coordinates, the three-dimensional spatial coordinates of the sound source are accurately calculated. Multiple sound source coordinate results are obtained through multi-microphone array combination calculations, thereby achieving accurate judgment and high-precision localization of the environmental sound source and its location.

Description

Technical Field

[0001] This application relates to the field of robotics, and more specifically, to a sound perception and localization method for a humanoid robot. Background Technology

[0002] In the current era of rapid iteration in artificial intelligence technology, robotics, as a core application area of ​​AI, has ushered in a new stage of comprehensive development. Industrial robots, with their high precision and stability, have been deeply integrated into industrial sectors such as manufacturing and logistics, playing an irreplaceable role in automated production, heavy cargo handling, and high-risk environment operations, greatly improving industrial production efficiency and intelligence. Humanoid robots are also in rapid development and already possess walking, object handling, obstacle recognition, and speech semantic functions; however, no humanoid robot yet can accurately determine the source and location of environmental sounds.

[0003] To overcome the above-mentioned shortcomings, existing technology 1 (Chinese patent with announcement number CN108089154B and publication date of 2021-06-11) provides a distributed sound source detection method and a sound detection robot based on the method. Compared with the traditional single-robot sound source detection system, the multi-robot collaborative sound source detection system based on distributed sound source localization technology has advantages such as a larger detection range, higher detection accuracy, stronger environmental adaptability and fault tolerance. As a new means of environmental perception for group robots, it greatly improves the robot's environmental perception capability and lays a good foundation for intelligent robot formation collaboration. Prior art 2 (Chinese patent with publication number CN109318232A and publication date of 2019-02-12) discloses a multi-sensor system for industrial robots. This system recognizes received voice information and generates executable commands for the industrial robot; it collects image information of the industrial robot's working environment and adjusts the robot's motion behavior in real time based on the image information; it collects current from servo motors to obtain joint torque and performs closed-loop force control drag teaching; it calculates the external torque applied to the industrial robot in real time to determine if a collision has occurred; and it interacts with data information in real time, processing and controlling the data. This allows traditional industrial robots to possess the teaching convenience and operational safety of collaborative robots while retaining their high speed and high precision in multi-sensoring of torque, sound, and vision.

[0004] The aforementioned institutions have achieved sound localization by utilizing distributed sound source localization or through a perception system. However, the existing perception system of humanoid robots still has key technological shortcomings. It has not yet achieved accurate judgment and localization of the source and location of environmental sounds. Sound perception and sound source localization are important capabilities for humans to perceive the external environment and achieve efficient interaction. The lack of this function makes the environmental perception ability of humanoid robots significantly different from that of humans, which greatly limits their adaptability and intelligence level in complex environments. Summary of the Invention

[0005] This application provides a sound perception and localization method for humanoid robots, which can be implemented.

[0006] In a first aspect, this application provides a sound perception and localization method for a humanoid robot, the sound perception and localization method for a humanoid robot comprising the following steps: S1. Scientific layout of microphone array and basic sound acquisition design; S2. Human-like sound signal preprocessing based on A-weighted sound level; S3. Sound source three-dimensional position calculation of point source radiation sound field algorithm; S4. Multi-dimensional sound source localization accuracy optimization strategy.

[0007] In this embodiment, the scientific layout and basic sound acquisition design of the S1 microphone array includes: S1-1, layout principles and position planning. The position planning uses 4 microphones to construct a basic three-dimensional sampling array. The sound field of the sound source is sampled by using 4 microphones, and the position of the sound source is determined by the point sound source radiation sound field algorithm.

[0008] In this embodiment, the scientific layout and basic sound acquisition design of the S1 microphone array includes: S1-2, hardware selection and basic acquisition function implementation, using a high-precision omnidirectional condenser microphone as the acquisition unit, matching the frequency range of human audible sound, to ensure that the sound signal can still be acquired without distortion in complex environments.

[0009] In this embodiment, the scientific layout and basic sound acquisition design of the S1 microphone array includes: S1-3, basic acquisition workflow, where the microphones synchronously capture sound signals in the environment, and after signal conditioning and digital conversion, output the original sound signal data and accurate sound pressure amplitude data of each measurement point in real time, along with identification information such as microphone number and sampling timestamp, to provide a high-fidelity and standardized data source for subsequent signal processing and algorithm calculation.

[0010] In this embodiment, the human-like sound signal preprocessing of the A-weighted sound level in S2 mainly adopts the A-weighted sound level that conforms to the hearing characteristics of the human ear as the processing standard, and performs digital weighting processing on the original sound signal collected by the microphone.

[0011] In this embodiment, the processing logic of the human-like sound signal preprocessing of the A-weighted sound level in S2 is as follows: the A-weighted network is used to perform differentiated gain adjustment on different frequency bands of the original sound signal, the low frequency signal of 20Hz to 500Hz and the high frequency signal of 16kHz to 20kHz that are not sensitive to the human ear are reasonably attenuated, the mid frequency signal of 2kHz to 4kHz that is most sensitive to the human ear is slightly amplified or processed without attenuation, and the DC component and high frequency interference noise in the sound signal are filtered out at the same time, and finally the human-like sound signal that conforms to the characteristics of human hearing perception is output.

[0012] In this embodiment, the three-dimensional position calculation of the sound source in the point sound source radiation sound field algorithm in S3 is mainly based on the sound radiation theory of the radiating sound field of the pulsating sphere source, and combined with the point sound source approximation model to realize the calculation of the sound source coordinates, so as to accurately solve the three-dimensional spatial coordinates of the sound source and realize the determination of the sound source position.

[0013] In this embodiment, the S4 multi-dimensional sound source localization accuracy optimization strategy is to arrange multiple microphones in the sound field to form an array acquisition structure, obtain multiple sound source coordinate results by combining multiple sets of microphones, and then use mathematical processing methods such as arithmetic average and weighted average to optimize and fit the multiple sets of coordinate results, effectively reducing the localization deviation caused by single microphone sampling error and environmental noise interference, and achieving higher accuracy sound source localization.

[0014] In this embodiment, the humanoid robot sound perception and positioning system includes: a sound acquisition module: its core function is to complete the distortion-free acquisition of environmental sound signals, accurate measurement of sound pressure amplitude, and preliminary encapsulation of raw data, providing a high-fidelity data source for subsequent processing; a signal preprocessing module: its core function is to digitally condition and human-like weighted process the raw sound signals output by the acquisition module, filter out environmental noise interference, and provide high-quality data input for algorithm calculation; an algorithm calculation module: based on the received human-like sound pressure data, combined with preset microphone coordinates, and using the pulsating sphere source radiation sound field theory, point source approximation model, and spatial coordinate calculation algorithm, it completes the real-time calculation, accuracy optimization, and result verification of the three-dimensional coordinates of the sound source, and is the core unit for achieving high-precision sound source positioning; and a result output module: it completes the reception, parsing, and format conversion of the sound source positioning results, and outputs the result data in a standardized manner according to the communication protocol of the humanoid robot main control system, while also realizing system status monitoring, fault reporting, and data log storage.

[0015] In this embodiment, the sound acquisition module includes a microphone array unit, a signal conditioning submodule, and a data acquisition submodule; the signal preprocessing module includes an embedded processing unit, an A-weighting hardware acceleration submodule, a noise suppression submodule, and a data caching and interface submodule; the algorithm calculation module includes a main computing unit, an algorithm storage submodule, an environmental perception and adaptation submodule, and a communication and control interface; and the result output module includes a main control interaction unit, a multi-protocol communication submodule, a status monitoring and alarm submodule, and a data log submodule.

[0016] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The A-weighting network is used to perform differential gain adjustment on different frequency bands of the original sound signal. The low-frequency signal of 20Hz to 500Hz and the high-frequency signal of 16kHz to 20kHz, which are not sensitive to human hearing, are reasonably attenuated. The mid-frequency signal of 2kHz to 4kHz, which is most sensitive to human hearing, is slightly amplified or processed without attenuation. At the same time, the DC component and high-frequency interference noise in the sound signal are filtered out, and finally a human-like sound signal that conforms to the characteristics of human hearing perception is output.

[0017] By introducing A-weighted sound level to weight the sound signal, the robot's sound perception frequency characteristics and intensity perception characteristics are highly consistent with those of the human ear. This breaks through the limitations of traditional robots' indiscriminate sound acquisition, realizes human-like sound perception, and enables the robot to more accurately capture sound information that humans care about, thereby improving its ability to recognize effective sound signals in complex environments.

[0018] Based on the sound radiation theory of pulsating sphere source radiation sound field and the point source approximation model, combined with the sound pressure data collected by the microphone array and preset coordinates, the three-dimensional spatial coordinates of the sound source can be accurately calculated, providing core algorithm support for robot sound source location. The point source localization algorithm based on the pulsating sphere source radiation sound field theory, combined with the three-dimensional spatial sampling of four basic microphones, achieves accurate determination of the sound source location through rigorous mathematical equation solving; at the same time, it supports multi-microphone array expansion and mathematical optimization processing, which can further improve the positioning accuracy and meet the positioning needs of complex environments.

[0019] By combining multi-microphone arrays and using mathematical optimization methods such as arithmetic averaging and weighted averaging, the positioning deviation caused by single microphone sampling error and environmental noise interference can be effectively reduced, significantly improving the overall accuracy of sound source positioning and ensuring that the robot can accurately determine the direction and distance of the sound source in practical application scenarios.

[0020] This technology adds crucial sound source perception and localization capabilities to humanoid robots, improves their environmental perception system, and enables robots to accurately perceive the source and location of external sounds. It provides core technical support for realizing advanced functions such as proactive interaction, environmental judgment, and hazard warning, and has significant theoretical and practical application value for the development of humanoid robots towards higher intelligence and greater anthropomorphism.

[0021] Therefore, this application can realize the source and location of environmental sound. First, a basic three-dimensional sampling array is constructed by scientifically arranging four microphones. High-precision omnidirectional condenser microphones are selected to ensure matching with the frequency range of human hearing, achieving distortion-free acquisition of environmental sound signals and simultaneously outputting raw sound signal data, accurate sound pressure amplitude data, and related identification information. Next, the raw sound signal is preprocessed using A-weighted sound level, which conforms to the characteristics of human hearing. Differential gain adjustment is performed on different frequency bands through an A-weighted network to attenuate low-frequency and high-frequency signals that are insensitive to the human ear, and amplify or retain sensitive mid-frequency signals. The signal is processed in a frequency band, while DC components and high-frequency interference noise are filtered out to output a human-like sound signal. Then, based on the theory of pulsating spherical source radiation sound field and point source approximation model, combined with human-like sound pressure data collected by the microphone array and preset coordinates, the three-dimensional spatial coordinates of the sound source are accurately calculated. Finally, multiple sound source coordinate results are obtained by combining multiple microphone arrays, and mathematical processing methods such as arithmetic mean and weighted average are used to optimize and fit the results, effectively reducing the positioning deviation caused by single microphone sampling error and environmental noise interference, thereby achieving accurate judgment and high-precision positioning of the source of environmental sound.

[0022] In summary, the technical solution adopted in this application can realize the source and location of environmental sound. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this embodiment of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of a sound perception and localization method for a humanoid robot provided in this application; Figure 2 This is a system block diagram of a sound perception and localization method for a humanoid robot provided in this application; Figure 3 This is an exemplary flowchart of a humanoid robot sound perception and localization method provided in this application; Figure 4This is an exemplary flowchart of a humanoid robot sound perception and positioning system provided in this application. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] This application provides a method for sound perception and localization of a humanoid robot, the core of which is through...

[0027] Example 1: To better understand the above technical solution, the following will provide a detailed description of the technical solution in conjunction with the accompanying drawings and specific implementation methods. (Refer to...) Figure 1 As shown in the figure, this is a schematic diagram of a humanoid robot sound perception and localization method according to this embodiment of the present application. The humanoid robot sound perception and localization method includes the following steps: It should be noted that S1, the scientific layout and basic sound acquisition design of the microphone array; S1, the scientific layout and basic sound acquisition design of the microphone array, includes: S1-1, layout principles and location planning, the location planning uses 4 microphones to construct a basic three-dimensional sampling array, the sound field of the sound source is sampled by using 4 microphones, and the sound source location is determined by the point sound source radiation sound field algorithm; S1-2, hardware selection and implementation of basic acquisition functions, high-precision omnidirectional condenser microphones are selected as acquisition units, matched with the frequency range of human audible sound, to ensure that the sound signal can be acquired without distortion even in complex environments; S1-3, basic acquisition workflow, the microphones synchronously capture the sound signals in the environment, after signal conditioning and digital conversion, the original sound signal data and accurate sound pressure amplitude data of each measurement point are output in real time, along with identification information such as microphone number and sampling timestamp, to provide a high-fidelity and standardized data source for subsequent signal processing and algorithm calculation.

[0028] It should be noted that S2, human-like sound signal preprocessing based on A-weighted sound level; the human-like sound signal preprocessing based on A-weighted sound level in S2 mainly uses A-weighted sound level that conforms to the characteristics of human hearing as the processing standard, and performs digital weighting processing on the original sound signal collected by the microphone. The processing logic of the human-like sound signal preprocessing based on A-weighted sound level in S2 is as follows: differential gain adjustment is performed on different frequency bands of the original sound signal through the A-weighting network, the low frequency signal of 20Hz~500Hz and the high frequency signal of 16kHz~20kHz that are not sensitive to the human ear are reasonably attenuated, the mid frequency signal of 2kHz~4kHz that is most sensitive to the human ear is slightly amplified or processed without attenuation, and the DC component and high frequency interference noise in the sound signal are filtered out at the same time, and finally the human-like sound signal that conforms to the characteristics of human hearing perception is output.

[0029] In this embodiment, according to the sound radiation theory of the pulsating spherical source radiating sound field, the sound pressure amplitude at any point in the spherical sound source radiating sound field is: …………(0), Where r is the distance from the measuring point to the sound source in the radiated sound field, r0 is the radius of the spherical sound source, ua is the vibration velocity amplitude of the sound source, and the rest are the air transmission constants. When r0 is very small, the spherical sound source shrinks to a point sound source, and the approximate expression for the sound pressure in the radiated sound field of the point sound source is as follows: …………………(1) As shown in the figure above, suppose there is a sound source at point O, radiating a sound field in all directions, with the coordinates of the sound source being (x, y, z). One microphone is placed at each of positions A, B, C, and D in the sound field. The sound pressure amplitudes P1, P2, P3, and P4 at points A, B, C, and D can be measured. According to formula (1): Through transformation, we obtain: …………………(2) According to the formula for the distance between two points: …………………(3) In practice, by substituting equation (3) into (2), three equations are obtained, containing three unknowns x, y, and z. The coordinates of the other four points A, B, C, and D are known, and the sound pressures P1, P2, P3, and P4 at the four points are measured by the microphone. The coordinates (x, y, z) of the sound source can be solved, thereby determining the location of the sound source.

[0030] To achieve higher sound source localization accuracy, multiple microphones can be arranged in the sound field to obtain multiple sound source coordinates. Mathematical processing, such as averaging, can then be applied to these multiple sound source coordinates to obtain even higher sound source localization accuracy.

[0031] Since the human ear's perception of sound is selective, the human ear can perceive sound frequencies ranging from 20 to 20,000 Hz, and the perceived intensity at each frequency point is different. Currently, the most consistent method with human ear's perception of audio is A-weighted sound level. Therefore, in this system, the sampled sound signal is processed by an A-weighted network at the front end of the calculation.

[0032] In this embodiment, by introducing A-weighted sound level to weight the sound signal, the robot's sound perception frequency characteristics and intensity perception characteristics are highly consistent with those of the human ear. This breaks through the limitations of traditional robots' indiscriminate sound acquisition, realizes human-like sound perception, and enables the robot to more accurately capture sound information that humans care about, thereby improving its ability to recognize effective sound signals in complex environments.

[0033] S3, Calculation of the three-dimensional position of the sound source by the point source radiation sound field algorithm; The calculation of the three-dimensional position of the sound source by the point source radiation sound field algorithm in S3 is mainly based on the sound radiation theory of the radiating sound field of the pulsating sphere source, and combined with the point source approximation model to calculate the coordinates of the sound source, so as to accurately solve the three-dimensional spatial coordinates of the sound source and realize the determination of the position of the sound source.

[0034] In this embodiment, based on the sound radiation theory of the pulsating sphere source radiating sound field and the point source approximation model, combined with the sound pressure data collected by the microphone array and preset coordinates, the three-dimensional spatial coordinates of the sound source can be accurately calculated, providing core algorithm support for the robot to locate the sound source position. The point source localization algorithm based on the pulsating sphere source radiating sound field theory, combined with the three-dimensional spatial sampling of four basic microphones, achieves accurate determination of the sound source position through rigorous mathematical equation solving; at the same time, it supports multi-microphone array expansion and mathematical optimization processing, which can further improve the positioning accuracy and meet the positioning needs of complex environments.

[0035] S4. Multi-dimensional sound source localization accuracy optimization strategy: The S4 multi-dimensional sound source localization accuracy optimization strategy involves arranging multiple microphones in the sound field to form an array-type acquisition structure. Multiple sound source coordinate results are obtained by combining multiple sets of microphones. Then, mathematical processing methods such as arithmetic average and weighted average are used to optimize and fit the multiple sets of coordinate results, effectively reducing the localization deviation caused by single microphone sampling error and environmental noise interference, and achieving higher accuracy sound source localization.

[0036] In this embodiment, by combining multi-microphone arrays and mathematical optimization methods such as arithmetic average and weighted average, the positioning deviation caused by single microphone sampling error and environmental noise interference can be effectively reduced, significantly improving the overall accuracy of sound source positioning and ensuring that the robot can accurately determine the direction and distance of the sound source in actual application scenarios.

[0037] In summary, the technical solution adopted in this application can identify the source and location of ambient sound. First, a basic three-dimensional sampling array is constructed by scientifically arranging four microphones. High-precision omnidirectional condenser microphones are selected to ensure matching with the frequency range of human hearing, achieving distortion-free acquisition of ambient sound signals and simultaneously outputting raw sound signal data, accurate sound pressure amplitude data, and related identification information. Next, the raw sound signal is preprocessed using A-weighted sound levels that conform to the characteristics of human hearing. Differential gain adjustment is performed on different frequency bands through an A-weighted network to attenuate low-frequency and high-frequency signals that are insensitive to the human ear, while amplifying or retaining sensitive signals. The system detects mid-frequency signals while filtering out DC components and high-frequency interference noise, outputting a human-like sound signal. Then, based on the theory of pulsating spherical source radiation sound field and point source approximation model, combined with human-like sound pressure data collected by the microphone array and preset coordinates, the three-dimensional spatial coordinates of the sound source are accurately calculated. Finally, multiple sound source coordinate results are obtained by combining multiple microphone arrays, and mathematical processing methods such as arithmetic average and weighted average are used to optimize and fit the results, effectively reducing the positioning deviation caused by single microphone sampling error and environmental noise interference, thereby achieving accurate judgment and high-precision positioning of the source of environmental sound.

[0038] Example 2: This application provides a humanoid robot sound perception and positioning system. The humanoid robot sound perception and positioning system includes: a sound acquisition module: its core function is to complete the distortion-free acquisition of environmental sound signals, accurate measurement of sound pressure amplitude, and preliminary encapsulation of raw data, providing a high-fidelity data source for subsequent processing; a signal preprocessing module: its core function is to digitally condition and human-like weighted process the raw sound signals output by the acquisition module, filter out environmental noise interference, and provide high-quality data input for algorithm calculation; an algorithm calculation module: based on the received human-like sound pressure data, combined with preset microphone coordinates, and through the pulsating sphere source radiation sound field theory, point source approximation model, and spatial coordinate calculation algorithm, it completes the real-time calculation, accuracy optimization, and result verification of the three-dimensional coordinates of the sound source, and is the core unit for achieving high-precision sound source positioning; and a result output module: it completes the reception, parsing, and format conversion of the sound source positioning results, and outputs the result data in a standardized manner according to the communication protocol of the humanoid robot main control system, while also realizing system status monitoring, fault reporting, and data log storage.

[0039] The sound acquisition module includes a microphone array unit, a signal conditioning submodule, and a data acquisition submodule; the signal preprocessing module includes an embedded processing unit, an A-weighting hardware acceleration submodule, a noise suppression submodule, and a data caching and interface submodule; the algorithm calculation module includes a main computing unit, an algorithm storage submodule, an environmental perception and adaptation submodule, and a communication and control interface; the result output module includes a main control interaction unit, a multi-protocol communication submodule, a status monitoring and alarm submodule, and a data log submodule.

[0040] In this embodiment, the process is divided into a perception stage, an adaptation stage, a computation stage, and an interaction stage. The microphone array of the sound acquisition module captures ambient sound signals. After signal conditioning and AD sampling, the raw sound pressure data is sent to the signal preprocessing module. The signal preprocessing module performs digital conditioning, A-weighting human-like processing, and noise suppression on the raw data, and outputs high-quality sound pressure data to the algorithm calculation module. The algorithm calculation module corrects the acoustic constants in combination with environmental parameters, calculates and optimizes the sound source coordinates through the core algorithm, and outputs the final positioning result to the result output module. The result output module standardizes the positioning result and sends it to the robot's main control system, while recording logs, monitoring system status, and responding to reverse commands from the main control system.

[0041] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0042] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compactdisc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0043] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

Claims

1. A method for sound perception and localization of a humanoid robot, characterized in that, The humanoid robot sound perception and localization method includes the following steps: S1. Scientific layout of the microphone array and basic sound acquisition design; S2. Human-like sound signal preprocessing based on A-weighted sound level; S3, Solving the three-dimensional position of the sound source using the point source radiation sound field algorithm; S4. Multi-dimensional sound source localization accuracy optimization strategy.

2. The humanoid robot sound perception and localization method as described in claim 1, characterized in that, The scientific layout and basic sound acquisition design of the S1 microphone array include: S1-1, Layout Principles and Location Planning: The location planning uses four microphones to construct a basic three-dimensional sampling array. The sound field of the sound source is sampled by using four microphones, and the location of the sound source is determined by the point sound source radiation sound field algorithm.

3. The humanoid robot sound perception and localization method as described in claim 2, characterized in that, The scientific layout and basic sound acquisition design of the S1 microphone array include: S1-2. Hardware selection and basic acquisition function implementation: A high-precision omnidirectional condenser microphone is selected as the acquisition unit, which is matched with the frequency range of human hearing to ensure that the sound signal can be acquired without distortion even in complex environments.

4. The humanoid robot sound perception and localization method as described in claim 3, characterized in that, The scientific layout and basic sound acquisition design of the S1 microphone array include: S1-3, Basic Acquisition Workflow: The microphone synchronously captures sound signals in the environment. After signal conditioning and digital conversion, it outputs the original sound signal data and accurate sound pressure amplitude data of each measurement point in real time, along with identification information such as microphone number and sampling timestamp, providing a high-fidelity and standardized data source for subsequent signal processing and algorithm calculation.

5. The humanoid robot sound perception and localization method as described in claim 4, characterized in that, The human-like sound signal preprocessing of the A-weighted sound level in S2 mainly adopts the A-weighted sound level that conforms to the hearing characteristics of the human ear as the processing standard, and performs digital weighting processing on the original sound signal collected by the microphone.

6. The humanoid robot sound perception and localization method as described in claim 5, characterized in that, The processing logic for the human-like sound signal preprocessing of the A-weighted sound level in S2 is as follows: Differentiated gain adjustment is performed on different frequency bands of the original sound signal through the A-weighted network. The low-frequency signal of 20Hz~500Hz and the high-frequency signal of 16kHz~20kHz, which are not sensitive to the human ear, are reasonably attenuated. The mid-frequency signal of 2kHz~4kHz, which is most sensitive to the human ear, is slightly amplified or processed without attenuation. At the same time, the DC component and high-frequency interference noise in the sound signal are filtered out, and finally, a human-like sound signal that conforms to the characteristics of human hearing perception is output.

7. The humanoid robot sound perception and localization method as described in claim 6, characterized in that, The three-dimensional position calculation of the sound source in the S3 point source radiation sound field algorithm is mainly based on the sound radiation theory of the pulsating spherical source radiation sound field, and combined with the point source approximation model to realize the calculation of the sound source coordinates, so as to accurately solve the three-dimensional spatial coordinates of the sound source and realize the determination of the sound source position.

8. The humanoid robot sound perception and localization method as described in claim 7, characterized in that, The S4 multi-dimensional sound source localization accuracy optimization strategy involves arranging multiple microphones in the sound field to form an array-type acquisition structure. Multiple sound source coordinates are obtained by combining multiple sets of microphones. Then, mathematical processing methods such as arithmetic average and weighted average are used to optimize and fit the multiple sets of coordinates. This effectively reduces the localization deviation caused by single microphone sampling error and environmental noise interference, and achieves higher accuracy sound source localization.

9. A humanoid robot sound perception and localization system, used to execute a humanoid robot sound perception and localization method as described in any one of claims 1 to 8, characterized in that, The humanoid robot sound perception and positioning system includes: Sound acquisition module: Completes the distortion-free acquisition of ambient sound signals, accurate measurement of sound pressure amplitude, and preliminary packaging of raw data, providing a high-fidelity data source for subsequent processing; Signal preprocessing module: performs digital conditioning and human-like weighted processing on the raw acoustic signal output by the acquisition module, filters out environmental noise interference, and provides high-quality data input for algorithm calculation; Algorithm calculation module: Based on the received human-like sound pressure data and combined with the preset microphone coordinates, it completes the real-time calculation, accuracy optimization and result verification of the three-dimensional coordinates of the sound source through the pulsating sphere source radiation sound field theory, point source approximation model and spatial coordinate calculation algorithm. It is the core unit for realizing high-precision sound source positioning. The output module receives, parses, and converts the sound source localization results, and outputs the results data in a standardized manner according to the communication protocol of the humanoid robot's main control system. It also realizes system status monitoring, fault reporting, and data log storage.

10. A humanoid robot sound perception and positioning system as described in claim 9, characterized in that, The sound acquisition module includes a microphone array unit, a signal conditioning submodule, and a data acquisition submodule; The signal preprocessing module includes an embedded processing unit, an A-weighting hardware acceleration submodule, a noise suppression submodule, and a data buffer and interface submodule; The algorithm calculation module includes a main computing unit, an algorithm storage submodule, an environmental perception and adaptation submodule, and a communication and control interface. The result output module includes a main control interaction unit, a multi-protocol communication submodule, a status monitoring and alarm submodule, and a data log submodule.

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