Vehicle noise masking apparatus and method

Abstract

A vehicle noise masking apparatus and method are disclosed. The vehicle noise masking apparatus includes: a database configured to store a sound source; a sound source classification unit configured to classify sound sources into a first sound source group and a second sound source group; a communication unit; a first processing unit configured to analyze a composition of the noise measurement signal based on the driving information and classify the noise measurement signal into vehicle-related noise and vehicle-independent noise, a second processing unit configured to extract a first masking sound source similar to the vehicle-related noise from the first sound source group, a third processing unit configured to extract a second masking sound source similar to the vehicle-independent noise from the second sound source group, and a fourth processing unit configured to extract a second masked sound source similar to the vehicle-independent noise from the second sound source group, and a fourth processing unit configured to generate a third masked sound source using at least one of the first masked sound source and the second masked sound source.

Description

[0001] Cross-references to related applications

[0002] This application claims priority to Korean Patent Application No. 10-2024-0109637, filed on August 16, 2024, the entire contents of which are incorporated herein by reference for all purposes. Technical Field

[0003] This disclosure relates to vehicle noise masking devices and methods. Background Technology

[0004] A significant amount of noise can be generated while a vehicle is in motion, and this noise may be transmitted to the driver and passengers. To reduce noise, sound-absorbing or sound-insulating materials can be added to essentially block the transmission path of the noise source, or logic for noise cancellation can be examined. However, these techniques suffer from increased costs and, even when applied to reduce the various types of noise generated in the vehicle's driving environment, their noise reduction effects are often not significant. Therefore, there is a need to develop a technique for classifying and masking the noise generated during vehicle operation.

[0005] The information included in this background is intended only to enhance the understanding of the general background of this disclosure and should not be construed as an admission or suggestion in any form that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0006] Various aspects of this disclosure are intended to provide vehicle noise masking devices and methods configured to remove or reduce various noises generated while a vehicle is in motion.

[0007] According to an exemplary embodiment of this disclosure, a vehicle noise masking device is provided, comprising: a database configured to store sound sources; a sound source classification unit configured to classify sound sources into a first sound source group and a second sound source group; a communication unit configured to collect noise measurement signals from a microphone installed on the vehicle and collect vehicle driving information via a controller local area network (CAN); a first processing unit configured to analyze the components of the noise measurement signals based on the driving information and classify the noise measurement signals into vehicle-related noise and vehicle-independent noise; a second processing unit configured to extract a first masking sound source similar to vehicle-related noise from the first sound source group; a third processing unit configured to extract a second masking sound source similar to vehicle-independent noise from the second sound source group; and a fourth processing unit configured to generate a third masking sound source using at least one of the first masking sound source and the second masking sound source.

[0008] The sound source classification unit can classify sound source groups based on the length, repetition characteristics, overlap characteristics, and rhythm characteristics of the sound sources.

[0009] The first processing unit can classify vehicle-related noise and vehicle-independent noise by performing correlation analysis between driving information and noise measurement signals.

[0010] The second processing unit can extract from the first sound source group the sound source with the frequency pattern most similar to the frequency pattern of vehicle-related noise as the first masking sound source.

[0011] The second processing unit can analyze frequency patterns using each frequency band level and the proportion of vehicle-related noise.

[0012] The third processing unit can extract sound sources from the second sound source group that include frequency patterns similar to those of vehicle-independent noise as second masking sound sources.

[0013] The third processing unit can extract sound sources from the second sound source group that include a beat sound pattern similar to the impact sound pattern unrelated to vehicle noise as a second masking sound source.

[0014] The third processing unit can analyze the impact sound pattern using the generation cycle of noise, including vehicle-independent noise, at a preset threshold or with an intensity greater than the preset threshold.

[0015] The third processing unit can extract sound sources from the second sound source group that include frequency patterns of noise unrelated to the vehicle and patterns similar to impact sound patterns as second masking sound sources.

[0016] The third processing unit can select a sound source that includes a beat sound pattern similar to an impact sound pattern unrelated to vehicle noise, and extract a sound source that includes a frequency pattern similar to a frequency pattern unrelated to vehicle noise from the selected sound source as a second masking sound source.

[0017] The fourth processing unit can adjust the volume of the first masking sound source using the intensity of vehicle-related noise, and adjust the volume of the second masking sound source using the intensity of vehicle-related noise to generate the third masking sound source.

[0018] According to another exemplary embodiment of this disclosure, a vehicle noise masking method is provided, comprising: collecting noise measurement signals from a microphone installed on the vehicle by a communication unit and collecting vehicle driving information via a controller local area network (CAN); analyzing the composition of the noise measurement signals based on the driving information by a first processing unit; classifying the noise measurement signals into vehicle-related noise and vehicle-independent noise by the first processing unit; extracting a first masking sound source most similar to vehicle-related noise from a first sound source group by a second processing unit; extracting a second masking sound source similar to vehicle-independent noise from a second sound source group by a third processing unit; and generating a third masking sound source using at least one of the first masking sound source and the second masking sound source by a fourth processing unit.

[0019] The vehicle noise masking method may further include: before extracting the first masking sound source, a sound source classification unit classifies the sound source group based on the length, repetition characteristics, overlap characteristics and beat characteristics of the sound source, and stores the classified sound source group in a database.

[0020] The classification of noise measurement signals may include classifying vehicle-related noise and vehicle-independent noise by analyzing the correlation between driving information and noise measurement signals.

[0021] Extracting the first masking source may include: analyzing the frequency patterns of vehicle-related noise; comparing the frequency patterns of the first source group and the vehicle-related noise; and extracting the first masking source from the first source group that is most similar to the vehicle-related noise.

[0022] Frequency pattern analysis can include analyzing frequency patterns using the level of each frequency band and the composition ratio of vehicle-related noise.

[0023] Extracting a second masking sound source may include: extracting sound sources from the second sound source group that include frequency patterns of noise unrelated to the vehicle and patterns similar to impact sound patterns as second masking sound sources.

[0024] Extracting a second masking sound source may include: analyzing the frequency patterns and impact sound patterns of vehicle-independent noise; selecting a sound source that includes a beat sound pattern similar to the impact sound pattern of vehicle-independent noise; and selecting a sound source that includes a frequency pattern similar to the frequency pattern of vehicle-independent noise as the second masking sound source.

[0025] Analyzing impact sound patterns may include analyzing the generation cycle of noise with an intensity of at or above a preset threshold in vehicle-independent noise.

[0026] Generating a third masking sound source may include: adjusting the volume of a first masking sound source using the intensity of vehicle-related noise; adjusting the volume of a second masking sound source using the intensity of vehicle-independent noise; and generating a third masking sound source using at least one of the first and second masking sound sources with adjusted volume.

[0027] The methods and apparatus of this disclosure have other features and advantages that will be apparent from or set forth in more detail in the accompanying drawings and the detailed description which are incorporated herein and together serve to explain certain principles of this disclosure. Attached Figure Description

[0028] Figure 1 This is a block diagram of the configuration of a vehicle noise masking device according to an exemplary embodiment of the present disclosure;

[0029] Figure 2This is a view used to describe the operation of the vehicle noise masking device according to an embodiment;

[0030] Figure 3 This is a view used to describe the operation of the sound source classification unit according to the implementation method;

[0031] Figure 4 This is a view used to describe the operation of the second processing unit according to the embodiment;

[0032] Figure 5 , Figure 6 , Figure 7 and Figure 8 This is a view used to describe the operation of the third processing unit according to the embodiment; and

[0033] Figure 9A and Figure 9B This is a flowchart of a vehicle noise masking method according to an exemplary embodiment of the present disclosure.

[0034] It is understood that the accompanying drawings are not necessarily drawn to scale and present slightly simplified representations of the various features illustrating the basic principles of this disclosure. Specific design features of this disclosure (including, for example, specific dimensions, orientations, positions, and shapes) will be determined in part by the specific intended application and environment of use.

[0035] In the accompanying drawings, reference numerals throughout the drawings refer to the same or equivalent parts of this disclosure. Detailed Implementation

[0036] Reference will now be made in detail to various embodiments of this disclosure, examples of which are illustrated in the accompanying drawings and described below. Although this disclosure will be described in conjunction with exemplary embodiments thereof, it should be understood that this specification is not intended to limit this disclosure to those exemplary embodiments. On the contrary, this disclosure is intended to cover not only the exemplary embodiments thereof, but also various alternatives, modifications, equivalents, and other embodiments that may be included within the spirit and scope of this disclosure as defined by the appended claims.

[0037] Various exemplary embodiments of this disclosure will be described in detail below with reference to the accompanying drawings.

[0038] However, the technical spirit of this disclosure is not limited to the various exemplary embodiments described and can be implemented in various different forms, and one or more components of the exemplary embodiments of this disclosure can be selectively combined, substituted and used within the scope of the technical spirit of this disclosure.

[0039] Furthermore, unless the context clearly and explicitly defines otherwise, all terms used herein (including technical and scientific terms) are to be interpreted as having the meaning commonly understood by those skilled in the art, and the meaning of commonly used terms (such as those defined in common dictionaries) will be interpreted in light of the contextual meaning of prior art.

[0040] Furthermore, the terminology used in the exemplary embodiments of this disclosure is for descriptive purposes only and is not intended to limit the scope of this disclosure.

[0041] In this specification, unless the context specifically indicates otherwise, the singular form includes the plural form, and in the case of describing "at least one (or one or more) of A, B, and C", this may include at least one combination of all possible combinations of A, B, and C.

[0042] Furthermore, in the description of the components disclosed herein, terms such as “first”, “second”, “A”, “B”, “(a)” and “(b)” may be used.

[0043] These terms are used only to distinguish one component from another, and the nature, order, etc., of these components are not limited by these terms.

[0044] Furthermore, it should be understood that when the first component is referred to as “connected” or “coupled” to the second component, such a description may include cases where the first component is directly connected or coupled to the second component, and cases where the first component is connected or coupled to the second component and a third component is arranged therebetween.

[0045] Furthermore, when referring to a first component being formed or disposed "above" or "below" a second component, this description includes cases where the two components are formed or disposed in direct contact with each other, as well as cases where one or more other components are located between the two components. Additionally, when the first component is referred to as being formed "above or below" a second component, this description can include cases where the first component is formed on an upper or lower side relative to the second component.

[0046] Figure 1 This is a block diagram illustrating the configuration of a vehicle noise masking device according to an exemplary embodiment of the present disclosure, and Figure 2 This is a view used to describe the operation of a vehicle noise masking device according to an exemplary embodiment of this disclosure. (Refer to...) Figure 1 and Figure 2 The microphone 10 installed on vehicle 1 can measure noise generated when the vehicle is in motion. The microphone 10 can be installed inside or outside the vehicle to measure engine noise (or electric vehicle motor noise) generated in vehicle 1, external environmental noise (construction site noise, etc.), etc. Multiple microphones 10 can be installed inside and outside the vehicle.

[0047] A vehicle noise masking device 100 according to an exemplary embodiment of the present disclosure may include a communication unit 110, a processor 120, a memory 130, and an audio reproduction unit 140.

[0048] In exemplary embodiments of this disclosure, in addition to the above, these components may have different functions and capabilities, and in addition to the components described below, these components include additional components. Furthermore, in exemplary embodiments of this disclosure, each component may be implemented using one or more physically separate devices, or by one or more processors 120 or a combination of one or more processors 120 and software, and may not be clearly distinguished in particular operation as in the example shown.

[0049] The vehicle noise masking device 100 according to an exemplary embodiment of this disclosure can be implemented in logic circuitry using hardware, firmware, software, or a combination thereof, and can be implemented using a general-purpose or special-purpose computer. The device can be implemented using hard-wired devices, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), etc. Furthermore, the device can be implemented as a system-on-a-chip (SoC) including one or more processors 120 and a controller.

[0050] Furthermore, the vehicle noise masking device 100 can be installed on a computing device or server equipped with hardware elements in the form of software, hardware, or a combination thereof. The computing device or server can be various devices, including all or some of communication devices, such as a communication modem or wired / wireless communication network for communicating with different devices, a memory 130 for storing data for executing programs, a microprocessor for executing programs to perform determinations and instructions, etc.

[0051] Communication unit 110 can support vehicle noise masking devices to communicate with electronic control units (ECUs) installed on the vehicle. Communication unit 110 may include a transceiver for sending and receiving Controller Area Network (CAN) messages using the CAN protocol. Communication unit 110 can support vehicle noise masking devices 100 to communicate with external electronic devices (e.g., terminals and servers). Communication unit 110 may include wireless communication circuitry and / or wired communication circuitry.

[0052] The communication unit 110 can use CAN communication to receive driving information of the vehicle 1 from the ECU of the vehicle 1. For example, the driving information may include at least one of the following: motor revolutions per minute (RPM), pedal opening, vehicle speed, driving mode, and gear position. The communication unit 110 can send the collected driving information to the processor 120.

[0053] The memory 130 may include a database (DB) for storing sound sources. The database may store various types of sound source data that can be used for noise masking. For example, the sound source data may include the sound of a stream, the sound of ocean waves, dance music with a constant beat, classical music without a beat, etc.

[0054] Furthermore, memory 130 may be a non-transitory storage medium for storing instructions executed by processor 120. Memory 130 may include at least one of random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), programmable read-only memory (PROM), electrically erasable PROM (EEPROM), erasable PROM (EPROM), hard disk drive (HDD), solid-state drive (SSD), embedded multimedia card (eMMC), universal flash memory (UFS), and / or network memory.

[0055] In the exemplary embodiments of this disclosure, the first processing unit 121, the second processing unit 122, the third processing unit 123, the fourth processing unit 124 and the sound source classification unit 125 can be implemented through the same processing, and for the sake of convenience, the operation of each component will be described separately below.

[0056] Processor 120 may include at least one of a processing device such as an ASIC, a digital signal processor (DSP), a programmable logic device (PLD), an FPGA, a central processing unit (CPU), a microcontroller, and / or a microprocessor.

[0057] Here, in the exemplary embodiments of this disclosure, the first processing unit 121, the second processing unit 122, the third processing unit 123, the fourth processing unit 124, and the sound source classification unit 125 may be implemented as a single processor. Alternatively, the first processing unit 121, the second processing unit 122, the third processing unit 123, the fourth processing unit 124, and the sound source classification unit 125 may be implemented as a single processor.

[0058] The sound source classification unit 125 can classify sound sources into a first sound source group and a second sound source group, and store the first and second sound sources in a database (DB). For example, the sound source classification unit 125 can classify the sound sources stored in the database based on the length, repetition characteristics, overlap characteristics, and beat characteristics of the sound sources, and store the sound sources in the first and second sound source groups.

[0059] The sound source classification unit 125 can classify sound sources stored in the database into a first sound source group for masking vehicle-related noise and a second sound source group for masking vehicle-independent noise.

[0060] Figure 3This is a view used to describe the operation of a sound source classification unit according to an exemplary embodiment of this disclosure. See also: Figure 3 The sound source classification unit 125 can pre-classify sound source data stored in the database as suitable for masking vehicle-related noise or vehicle-independent noise, and store the sound source data into another sound source group. For example, the sound source classification unit 125 can classify shorter, repeatable, rhythmic sound sources that do not significantly impair other sound sources when reproduced by overlapping them into a second sound source group. For example, the second sound source group may include sound effects such as stream sounds and ocean wave sounds, or short pieces of music with strong beats.

[0061] Optionally, the sound source classification unit 125 can classify long, rhythmless sound sources that overlap with other sound sources and cause discomfort to the listener into a first sound source group. For example, the first sound source group may include classical music, songs with lyrics, pop songs, etc.

[0062] The sound source classification unit 125 can analyze the characteristics of the classified sound sources and store the characteristics together with the sound sources.

[0063] The sound source classification unit 125 can analyze the frequency patterns of the first sound source group and the second sound source group, and store the analysis results. The sound source classification unit 125 can classify the frequency pattern analysis results into root mean square (RMS) values ​​for each frequency band and store them, or classify the frequency pattern analysis results into Mel frequency cepstral coefficients (MFCCs) and store them.

[0064] The sound source classification unit 125 can perform a short-time Fourier transform (STFT) on multiple sound source data belonging to the first sound source group and the second sound source group, and use the size of each frequency band in the transformed sound source data to determine the ratio of the RMS size of each frequency band.

[0065] Alternatively, the sound source classification unit 125 can perform STFT on multiple sound source data belonging to the first sound source group and the second sound source group, and then determine MFCC.

[0066] The sound source classification unit 125 can classify sound source data into frames (e.g., 20ms to 40ms) and apply STFT to determine the spectral signal. The sound source classification unit 125 can apply a Mel filter bank to the determined spectral signal to determine the Mel spectral signal. The sound source classification unit 125 can apply cepstral analysis to the determined Mel spectral signal to determine the MFCC.

[0067] The sound source classification unit 125 can perform frequency pattern analysis on all sound source data belonging to the first sound source group and the second sound source group, classify the results of the analysis into RMS size or MFCC according to each frequency band, and display and store the RMS size or MFCC as composition ratio according to each frequency band.

[0068] Furthermore, the sound source classification unit 125 can analyze and store the beat tone patterns of the second sound source group. The sound source classification unit 125 can store the results of the beat tone pattern analysis as a beat tone generation period.

[0069] The sound source classification unit 125 can extract signals, including periodicity and a predetermined or greater volume, from sound source data belonging to the second sound source group to match the beat or indicate rhythm. The sound source classification unit 125 can store the period of the extracted signal as a beat sound period.

[0070] Optionally, when the sound source data is a MIDI sound source, the sound source classification unit 125 can separate the keyboard sound, serial sound and tapping sound components from the sound source data, determine the period of the separated tapping sound, and store the period of the separated tapping sound as the tapping sound period.

[0071] The sound source classification unit 125 can perform beat tone pattern analysis on all sound source data belonging to the second sound source group, and display and store the results as beat tone period.

[0072] Therefore, the sound source data belonging to the first sound source group may include frequency pattern information, and the sound source data belonging to the second sound source group may include frequency pattern information and beat tone pattern information and may be stored in a database.

[0073] The first processing unit 121 can analyze the composition of the noise measurement signal based on driving information and classify the noise measurement signal into vehicle-related noise and vehicle-independent noise.

[0074] In an exemplary embodiment of this disclosure, vehicle-related noise can be engine noise of a vehicle or motor noise of an electric vehicle, and can be a noise component that can be predicted by driving information. Vehicle-independent noise can be all noise components other than vehicle-related noise and can be environmental noise, such as construction site noise generated outside the vehicle.

[0075] The first processing unit 121 can classify vehicle-related noise and vehicle-independent noise by performing correlation analysis between driving information and noise measurement signals.

[0076] For example, the first processing unit 121 can classify noise that is highly correlated with the driving information in the noise measurement signal as vehicle-related noise by performing multiple correlation analyses between the vehicle speed and the motor revolutions per minute (RPM) value in the driving information and the sound pressure level in the noise measurement signal.

[0077] Alternatively, the first processing unit 121 may analyze driving information and vehicle-related noise using the RPM noise level of the noise measurement signal based on the main harmonic of the engine or the main harmonic of the motor.

[0078] For example, a 4-cylinder engine may have engine main harmonics, such as the 2nd, 4th, 6th, and 8th harmonics, and an 8-pole electric vehicle motor may have motor main harmonics, such as the 8th, 16th, 24th, 32nd, and 40th harmonics. The first processing unit 121 can transform the noise measurement signal to the frequency domain, determine the noise level based on the revolutions per minute (rpm) of each main harmonic of the engine or each main harmonic of the motor, and then inversely transform the noise measurement signal to the time domain to classify vehicle-related noise.

[0079] The first processing unit 121 can extract vehicle-related noise from the noise measurement signal separately and store the remaining signal after separating the vehicle-related noise from the noise measurement signal as vehicle-independent noise.

[0080] The second processing unit 122 can extract the first masked sound source that is most similar to the vehicle-related noise from the first sound source group.

[0081] For example, the second processing unit 122 can analyze frequency patterns using the frequency level of each frequency band and the composition ratio of vehicle-related noise.

[0082] The second processing unit 122 can extract from the first sound source group the sound source with the frequency pattern most similar to the frequency pattern of vehicle-related noise as the first masking sound source.

[0083] The second processing unit 122 can analyze the frequency patterns of vehicle-related noise and store the analysis results. The second processing unit 122 can classify the frequency pattern analysis results into RMS values ​​according to each frequency band and store the RMS values, or classify the above results into MFCC values.

[0084] The second processing unit 122 can perform STFT on vehicle-related noise and use the intensity of each frequency band to determine the ratio of the RMS magnitude of each frequency band in the vehicle-related noise transformed into frequency bands.

[0085] Alternatively, the second processing unit 122 may perform STFT on vehicle-related noise and then determine MFCC.

[0086] The second processing unit 122 can classify vehicle-related noise into frames (e.g., 20ms to 40ms) and apply STFT to determine the spectral signal. The second processing unit 122 can apply a Mel filter bank to the determined spectral signal to determine the Mel spectral signal. The second processing unit 122 can apply cepstral analysis to the determined Mel spectral signal to determine the MFCC.

[0087] Figure 4 This is a view used to describe the operation of a second processing unit according to an exemplary embodiment of this disclosure. See also: Figure 4The second processing unit 122 can perform frequency pattern analysis, classify the results of the analysis into RMS size according to each frequency band, and display and store the RMS size as composition ratio according to each frequency band.

[0088] The second processing unit 122 can compare the frequency patterns of vehicle-related noise with the frequency patterns of the first sound source group to determine the similarity.

[0089] For example, the second processing unit 122 can be configured to determine a correlation coefficient between the ratios of the RMS magnitudes of each frequency band to determine similarity. The second processing unit 122 can be configured to determine a correlation coefficient between the ratios of the RMS magnitudes of each frequency band of the vehicle-related noise frequency pattern to the ratios of the RMS magnitudes of the frequency bands of the first sound source group, as shown in Equation 1 below:

[0090] [Equation 1]

[0091]

[0092] In equation 1, r xy X represents the correlation coefficient. i Y represents the RMS magnitude of the vehicle-related noise in the i-th frequency band. i This represents the RMS value of the i-th frequency band of a sound source belonging to the first sound source group. This represents the average RMS magnitude of vehicle-related noise across the entire frequency band. denoted as the average RMS value of sound sources belonging to the first sound source group across the entire frequency band, and n represents the number of frequency bands.

[0093] The second processing unit 122 can be configured to determine that the higher the correlation coefficient determined by Equation 1, the higher the similarity between the vehicle-related noise and the corresponding sound source.

[0094] Alternatively, the second processing unit 122 can be configured to use MFCC to determine the similarity between vehicle-related noise and sound sources belonging to the first sound source group. The second processing unit 122 can be configured to use the KL divergence method to determine entropy to determine similarity.

[0095] The second processing unit 122 can transform the Mel spectrum obtained from the MFCC of the sound sources belonging to the first sound source group into a logarithmic scale, and based on the KL divergence method, determine the entropy (D) representing the difference between the distribution of vehicle-related noise and the sound sources belonging to the first sound source group according to Equation 2 below. KL ).

[0096] [Equation 2]

[0097]

[0098] In Equation 2, P represents the log-Mel spectrum of the sound source, Q represents the log-Mel spectrum of the vehicle-related noise, and D KL Entropy represents the difference between vehicle-related noise and the distribution of noise sources belonging to the first noise source group.

[0099] The second processing unit 122 can repeatedly determine D for all sound sources and vehicle-related noise belonging to the first sound source group. KL The mean and standard deviation of the normal distribution are determined using equations 3 and 4 below:

[0100] [Equation 3]

[0101]

[0102] [Equation 4]

[0103]

[0104] In equations 3 and 4, N(x|μ, σ) 2 ) represents the standardized D KL Pi represents the log-Mel spectrum of the i-th sound source (i = 1 to m (natural numbers)), Q represents the log-Mel spectrum of vehicle-related noise, and μ represents the D-values ​​of the m sound sources. KL The average value, σ represents the D of m sound sources. KL The standard deviation.

[0105] The second processing unit 122 can process the D between the i-th sound source and the vehicle-related noise. KL (That is,) substituting the standardized N value into Equation 3 yields the standardized probability density function. In this case, when D KL A larger value of indicates a lower correlation between vehicle-related noise and the sound source, while a smaller value indicates a higher correlation. Therefore, the second processing unit 122 can display and store D determined from Equation 2. KL Value minus standardized D KL The obtained value is used as the similarity value between vehicle-related noise and the sound source.

[0106] The second processing unit 122 can select the sound source with the highest similarity determined by the above method from the first sound source group, and extract the selected sound source as the first masking sound source.

[0107] The third processing unit 123 can extract a second masked sound source that is similar to vehicle-independent noise from the second sound source group.

[0108] The third processing unit 123 can analyze the frequency patterns and impact sound patterns of vehicle-independent noise.

[0109] The third processing unit 123 can analyze the frequency patterns of vehicle-independent noise and store the analysis results. The third processing unit 123 can classify the frequency pattern analysis results into the RMS value of each frequency band and store the RMS value, or classify the above results into MFCC and store the MFCC.

[0110] The third processing unit 123 can perform STFT on vehicle-independent noise and determine the ratio of the RMS magnitude of each frequency band using the magnitude of each frequency band of the vehicle-independent noise converted into frequency bands.

[0111] Alternatively, the third processing unit 123 may perform STFT on vehicle-independent noise and then determine MFCC.

[0112] The third processing unit 123 can classify vehicle-independent noise into frames and apply STFT to determine the spectral signal. The third processing unit 123 can apply a Mel filter bank to the determined spectral signal to determine the Mel spectral signal. The third processing unit 123 can apply cepstral analysis to the determined Mel spectral signal to determine MFCC.

[0113] The third processing unit 123 can perform frequency pattern analysis on vehicle-independent noise, classify the results of the analysis into the RMS size or MFCC of each frequency band, and display and store the RMS size or MFCC as the composition ratio of each frequency band.

[0114] Furthermore, the third processing unit 123 can analyze the impact sound patterns of vehicle-independent noise and store the analysis results. The third processing unit 123 can store the results of the impact sound pattern analysis as the impact sound generation cycle.

[0115] The third processing unit 123 may be configured to determine whether vehicle-independent noise is a periodic signal. The third processing unit 123 may be configured to determine that a signal with a predetermined magnitude or higher sound pressure level is a periodic signal when a signal with a predetermined magnitude or higher sound pressure level is repeatedly generated at time intervals.

[0116] Figure 5 , Figure 6 , Figure 7 and Figure 8 This is a view used to describe the operation of a third processing unit according to an exemplary embodiment of this disclosure. Also refer to... Figure 5 and Figure 6 The third processing unit 123 can divide vehicle-independent noise into a minimum time (e.g., 0.1 seconds) that can be identified as impact noise by a human and determine the sound pressure level based on each time interval of the division. The third processing unit 123 can be configured to identify each signal whose sound pressure level exceeds a preset impact sound determination reference level as impact sound.

[0117] The third processing unit 123 can be configured to determine the determined generation interval of the impact sound and store the generation interval as the impact sound generation cycle.

[0118] See also Figure 7 When the interval between impact sound generation is variable, the third processing unit 123 can count each cycle in which impact sounds are generated at regular intervals or count the time during which impact sounds are generated a constant number of times (e.g., the time during which impact sounds are generated 5 times), and store the value obtained by arithmetically averaging the counted cycles as the impact sound cycle. For example, the third processing unit 123 can be configured to determine the average value of the time interval between impact sounds generated between 0 seconds and 2.5 seconds and store the average value as the impact sound generation cycle during the corresponding time period. Furthermore, the third processing unit 123 can be configured to determine the average value of the time interval between impact sounds generated between 2.5 seconds and 5 seconds and store the average value as the impact sound generation cycle during the corresponding time period.

[0119] The third processing unit 123 can display and store the results of the impact sound pattern analysis as the impact sound cycle.

[0120] The third processing unit 123 can compare at least one of the frequency mode and the impact sound mode with the second sound source group to select the second masking sound source.

[0121] Also refer to Figure 8 When the impact sound exists in vehicle-independent noise, the third processing unit 123 can compare the impact sound patterns to select the second masking sound source.

[0122] Alternatively, when the impact sound is present in vehicle-independent noise, the third processing unit 123 may compare the frequency pattern with the impact sound pattern to select the second masking sound source.

[0123] Alternatively, when there is no impact sound in the vehicle-independent noise but the average sound pressure level of the vehicle-independent noise exceeds a preset threshold, the third processing unit 123 can compare frequency patterns to select the second masking sound source.

[0124] Alternatively, when there is no impact sound in the vehicle-independent noise and the average sound pressure level of the vehicle-independent noise does not exceed a preset threshold, the third processing unit 123 may not select the second masking sound source. In this case, as will be described below, the third masking sound source may be generated using only the first masking sound source.

[0125] For example, the third processing unit 123 can extract from the second sound source group the sound source whose beat tone pattern is most similar to the impact sound pattern of the vehicle-independent noise as the second masking sound source. That is, when it is determined that the impact sound exists in the vehicle-independent noise, the third processing unit 123 can compare the impact sound period of the vehicle-independent noise with the beat tone period of the sound source to extract the second masking sound source.

[0126] The third processing unit 123 can compare whether an integer multiple of the impact sound period of the vehicle-independent noise matches an integer multiple of the beat sound period of the sound source according to the following equation 5. The third processing unit 123 can be configured to determine that the corresponding sound source matches the vehicle-independent noise when at least one of the preset j (natural number) and k (natural number) values ​​satisfies equation 5.

[0127] [Equation 5]

[0128] If {abs(beat sound period × j – impact sound period × k) < allowed value}, then the beat tone pattern is matched.

[0129] Here, j and k represent set values ​​that ensure the beat sound matches the impact sound even when each of the beat sound and the impact sound corresponds to an integer multiple of each other, achieving a masking effect when they are integer multiples of each other. However, as the values ​​of j or k increase, the masking time decreases, thus reducing the masking effect. Therefore, the values ​​of j and k can be set to various values ​​depending on the masking performance, and can be set to values ​​less than 4, for example. That is, when the values ​​of j and k are set too small, the masking performance is improved, but it is difficult to select a matching sound source, and when the values ​​of j and k are set too large, several secondary masking sound sources are selected, but the masking performance can be relatively reduced. Therefore, based on the masking performance and the convenience of sound source selection, the values ​​of j and k can be set to natural numbers less than 4. Furthermore, the permissible value in Equation 5 can be set to 5% to 10% (e.g., 0.05 seconds) of the general impact sound period as an offset value.

[0130] The third processing unit 123 may select at least one second masking sound source that satisfies Equation 5. Since the second masking sound sources do not cause significant interference even when they overlap with each other, and do not make the listener feel uncomfortable, multiple second masking sound sources may be selected.

[0131] For example, the third processing unit 123 can extract sound sources from the second sound source group that include frequency patterns similar to those of vehicle-independent noise as second masking sound sources. That is, when the impact sound is not present in vehicle-independent noise but the average sound pressure level of vehicle-independent noise exceeds a preset threshold, the third processing unit 123 can compare frequency patterns to select the second masking sound source.

[0132] The process of comparing frequency patterns to select a second masking sound source is the same as the process of comparing frequency patterns and selecting a first masking sound source described above in the second processing unit 122, and its repeated description will be omitted. At this time, the third processing unit 123 may extract at least one second masking sound source including a preset similarity or higher similarity.

[0133] For example, the third processing unit 123 can extract from the second sound source group a sound source that includes the most similar frequency pattern of the vehicle-independent noise and the impact sound pattern as a second masking sound source. That is, when it is determined that the impact sound exists in the vehicle-independent noise, the third processing unit 123 can compare the impact sound pattern of the vehicle-independent noise with the beat sound pattern to select a sound source, and select a sound source that includes a frequency pattern similar to the frequency pattern of the vehicle-independent noise as a second masking sound source.

[0134] That is, the third processing unit 123 can first compare the impact sound of vehicle-independent noise with the beat pattern of the sound source to select at least one second masking sound source candidate group. The third processing unit can compare the frequency patterns of the second masking sound source candidate group with the frequency patterns of vehicle-independent noise to select a sound source with a frequency pattern similar to the second masking sound source. At this time, the third processing unit 123 can extract at least one second masking sound source with a preset similarity or higher similarity.

[0135] The fourth processing unit 124 can be configured to generate a third masking sound source using at least one of the first masking sound source and the second masking sound source. The fourth processing unit 124 can overlap the first masking sound source and the second masking sound source on the time axis to generate the third masking sound source.

[0136] The fourth processing unit 124 can adjust the volume of the first masking sound source using the intensity of vehicle-related noise and adjust the volume of the second masking sound source using the intensity of vehicle-independent noise. The fourth processing unit 124 can be configured to determine the magnitude of the masking sound source as an RMS value over the entire time period, and display and store the RMS value in decibels. Furthermore, the fourth processing unit 124 can be configured to determine the RMS magnitudes of vehicle-related noise and vehicle-independent noise within a predetermined time period, and display and store the RMS magnitudes in decibels.

[0137] The fourth processing unit 124 can adjust the RMS value of the first masking sound source based on the RMS value of the vehicle-related noise, and adjust the RMS value of the second masking sound source based on the RMS value of the vehicle-independent noise. The fourth processing unit 124 can be configured to determine the volume gain of the masking sound source such that the volume of the masking sound source can be output as greater than or equal to the noise volume, thereby maximizing the masking effect. The fourth processing unit can be configured to determine the volume gain of the masking sound source according to the following Equation 6.

[0138] [Equation 6]

[0139] Gain_masking=C_correction x RMS_noise(dB)÷RMS_making(dB)

[0140] In Equation 6, Gain_masking represents the volume gain of the first or second masking source, RMS_noise represents the RMS magnitude (dB) of vehicle-related or vehicle-independent noise, RMS_making represents the RMS magnitude (dB) of the first or second masking source, and C_correction represents the correction constant. The correction constant is a value used to adjust the volume of the noise and masking sources to make the volumes similar or have a large difference between them, and can have values ​​ranging from, for example, from 1 to 1.2.

[0141] The audio reproduction unit 140 can reproduce the third masked sound source generated by the fourth processing unit 124. The audio reproduction unit 140 can reproduce sound and output the sound to speakers installed inside the vehicle. The audio reproduction unit 140 can reproduce and output pre-stored or real-time streamed sound sources. The audio reproduction unit 140 may include an amplifier, a sound reproduction device, etc. The audio reproduction unit 140 can adjust the volume, pitch (sound quality), sound image, etc., of the sound according to instructions from the processor 120 to reproduce the sound. The sound reproduction device may include a digital signal processor (DSP) and / or a microprocessor. The amplifier can amplify the electrical signal of the sound reproduced by the sound reproduction device.

[0142] Figure 9A and Figure 9B This is a flowchart of a vehicle noise masking method according to an exemplary embodiment of the present disclosure.

[0143] refer to Figure 9A and Figure 9B The communication unit collects noise measurement signals from the microphone installed on the vehicle and collects vehicle driving information via CAN communication (S901).

[0144] Next, the first processing unit analyzes the components of the noise measurement signal based on the driving information (S902).

[0145] Next, the first processing unit classifies the noise measurement signal into vehicle-related noise and vehicle-independent noise (S903).

[0146] Next, the second processing unit analyzes the frequency pattern of vehicle-related noise (S904).

[0147] Next, the second processing unit compares the frequency patterns of the first sound source group with those of vehicle-related noise (S905).

[0148] Next, the second processing unit extracts the first masked sound source that is most similar to the vehicle-related noise from the first sound source group (S906).

[0149] The third processing unit uses a generation cycle analysis of the impact sound pattern (S907) including noise with an intensity equal to or greater than a preset threshold in vehicle-independent noise.

[0150] Next, when the impact sound pattern exists in vehicle-independent noise, the third processing unit compares the impact sound period of the vehicle-independent noise with the beat sound period of the sound source (S908 and S909).

[0151] Subsequently, the third processing unit selects at least one second masking sound source candidate group (S910 and S911) with a beat sound period similar to the impact sound period unrelated to vehicle noise.

[0152] Subsequently, the third processing unit compares the frequency patterns of the second masked sound source candidate group with the frequency patterns of vehicle-independent noise (S911).

[0153] Next, the third processing unit selects a sound source with a frequency pattern similar to that of the second masking sound source candidate group as the second masking sound source (S912).

[0154] Alternatively, when the impact sound is not present in the vehicle-independent noise but the average sound pressure level of the vehicle-independent noise exceeds a preset threshold, the third processing unit compares the frequency pattern of the vehicle-independent noise with the second sound source group (S913 and S914).

[0155] Subsequently, the third processing unit extracts the second masked sound source (S914 and S915) from the second sound source group that is most similar to vehicle-independent noise.

[0156] Alternatively, when there is no impact sound in the vehicle-independent noise and the average sound pressure level of the vehicle-independent noise does not exceed a preset threshold, the third processing unit does not select the second masking sound source (S916).

[0157] The processing of selecting the first masking sound source and the processing of selecting the second masking sound source can be performed simultaneously, or the processing of selecting either masking sound source can be performed before the processing of selecting the other masking sound source.

[0158] Next, the fourth processing unit generates a third masking sound source using at least one of the first and second masking sound sources. In this case, the fourth processing unit adjusts the volume of the first masking sound source using the intensity of vehicle-related noise, adjusts the volume of the second masking sound source using the intensity of vehicle-independent noise, and then generates the third masking sound source (S917).

[0159] Next, the audio reproduction unit reproduces the third masked sound source (S918).

[0160] The processor is configured to repeatedly execute the process of selecting a first masking sound source and a second masking sound source to generate a third masking sound source when the characteristics of the periodically collected noise measurement signal change beyond the error range.

[0161] According to exemplary embodiments of the present disclosure, a vehicle noise masking device and method can classify and mask each of the noises generated during vehicle operation based on the vehicle's driving correlation.

[0162] Therefore, it can significantly reduce the impact of noise on vehicle occupants.

[0163] In various exemplary embodiments of this disclosure, the memory and processor may be provided as a single chip or as separate chips.

[0164] In various exemplary embodiments of this disclosure, the scope of this disclosure includes software or machine-executable commands (e.g., operating systems, applications, firmware, programs, etc.) for enabling operation of methods according to various embodiments to be executed on a device or computer, including non-transitory computer-readable media containing such software or commands stored thereon and executable on a device or computer.

[0165] Software implementation may include software components (or elements), object-oriented software components, class components, task components, procedures, functions, properties, processes, subroutines, program code segments, drivers, firmware, microcode, data, databases, data structures, tables, arrays, and variables. Software, data, etc., may be stored in memory and executed by a processor. Memory or processor may employ a variety of means well known to those skilled in the art.

[0166] In addition, terms such as “unit” and “module” included in the specification refer to a unit for performing at least one function or operation, which can be implemented by hardware, software or a combination thereof.

[0167] In the flowchart described with reference to the accompanying drawings, the flowchart can be executed by a controller or processor. The order of operations in the flowchart can be changed, multiple operations can be combined, or any operation can be divided, and specific operations may not be executed. Furthermore, the operations in the flowchart can be executed sequentially, but not necessarily in a sequential order. For example, the order of operations can be changed, and at least two operations can be executed in parallel.

[0168] In the following text, the fact that hardware blocks are operatively connected may include the fact that direct and / or indirect connections are established between hardware blocks via wired and / or wireless means.

[0169] In exemplary embodiments of this disclosure, a vehicle may be referred to as a vehicle based on a concept that includes a variety of means of transportation. In some cases, a vehicle may be interpreted as being based not only on a variety of land vehicles (such as cars, motorcycles, trucks, and buses) that travel on roads, but also on a variety of means of transportation such as airplanes, drones, ships, etc.

[0170] For ease of explanation and precise definition of the appended claims, the features of the exemplary embodiments are described using the terms “upper,” “lower,” “inner,” “outer,” “upward,” “downward,” “above,” “below,” “front,” “rear,” “rear,” “inner side,” “outer side,” “inward,” “outer,” “internal,” “external,” “inner,” “outer,” “forward,” and “rearward”, with reference to the locations of such features shown in the accompanying drawings. It should be further understood that the term “connection” or its derivatives refer to both direct and indirect connections.

[0171] The term "and / or" can include a combination of multiple related listed items or any one of multiple related listed items. For example, "A and / or B" includes all three cases, such as "A", "B", and "A and B".

[0172] In exemplary embodiments of this disclosure, "at least one of A and B" may refer to "at least one of A or B" or "at least one of a combination of at least one of A and B". Furthermore, "one or more of A and B" may refer to "one or more of A or B" or "one or more of a combination of one or more of A and B".

[0173] In this specification, unless otherwise stated, singular expressions include plural expressions, unless the context clearly indicates otherwise.

[0174] In exemplary embodiments of this disclosure, it should be understood that terms such as “comprising” or “having” are intended to specify the presence of the features, quantities, steps, operations, elements, components or combinations thereof described in the specification, and do not preclude the possibility of adding or having one or more other features, quantities, steps, operations, elements, components or combinations thereof.

[0175] According to exemplary embodiments of this disclosure, components may be combined with each other to form a single unit, or some components may be omitted.

[0176] For purposes of illustration and description, the foregoing description of specific exemplary embodiments of this disclosure has been presented. They are not intended to be exhaustive or to limit this disclosure to the precise forms disclosed, and it will be apparent that many modifications and variations are possible in accordance with the foregoing teachings. Exemplary embodiments have been selected and described to illustrate certain principles of this disclosure and its practical application, enabling others skilled in the art to make and utilize various exemplary embodiments of this disclosure and their various alternatives and modifications. The scope of this disclosure is intended to be defined by the appended claims and their equivalents.

Claims

1. A vehicle noise masking device, comprising: The database is configured to store sound sources; The sound source classification unit is configured to classify the sound sources into a first sound source group and a second sound source group; The communication unit is configured to collect noise measurement signals from a microphone mounted on the vehicle and to collect driving information of the vehicle via Controller Area Network (CAN) communication; The first processing unit is configured to analyze the composition of the noise measurement signal based on the driving information and classify the noise measurement signal into vehicle-related noise and vehicle-independent noise. The second processing unit is configured to extract a first masked sound source from the first sound source group that is similar to the vehicle-related noise; The third processing unit is configured to extract a second masked sound source from the second sound source group that is similar to noise unrelated to the vehicle. as well as The fourth processing unit is configured to generate a third masking sound source using at least one of the first masking sound source and the second masking sound source.

2. The vehicle noise masking device according to claim 1, wherein, The sound source classification unit classifies the sound sources into the first sound source group and the second sound source group based on the length, repetition characteristics, overlap characteristics and rhythm characteristics of the sound sources.

3. The vehicle noise masking device according to claim 1, wherein, The first processing unit classifies the vehicle-related noise and the vehicle-independent noise by performing correlation analysis between the driving information and the noise measurement signal.

4. The vehicle noise masking device according to claim 1, wherein, The second processing unit extracts from the first sound source group the sound source whose frequency pattern is most similar to the frequency pattern of the vehicle-related noise as the first masking sound source.

5. The vehicle noise masking device according to claim 4, wherein, The second processing unit uses the level of each frequency band and the composition ratio of the vehicle-related noise to analyze the frequency pattern.

6. The vehicle noise masking device according to claim 1, wherein, The third processing unit extracts sound sources from the second sound source group that include frequency patterns similar to those of noise unrelated to the vehicle as the second masking sound sources.

7. The vehicle noise masking device according to claim 1, wherein, The third processing unit extracts sound sources from the second sound source group that include a beat tone pattern similar to the impact sound pattern unrelated to the vehicle, as the second masking sound source.

8. The vehicle noise masking device according to claim 7, wherein, The third processing unit uses the generation cycle of noise in the vehicle-independent noise, including noise with an intensity of a preset threshold or greater than the preset threshold, to analyze the impact sound pattern.

9. The vehicle noise masking device according to claim 1, wherein, The third processing unit extracts sound sources from the second sound source group that have a similar frequency pattern to the vehicle-independent noise and impact sound pattern as the second masking sound source.

10. The vehicle noise masking device according to claim 9, wherein, The third processing unit selects a sound source that includes a beat tone pattern similar to the impact sound pattern unrelated to the vehicle noise, and extracts a sound source that includes a frequency pattern similar to the frequency pattern unrelated to the vehicle noise from the selected sound source as the second masking sound source.

11. The vehicle noise masking device according to claim 1, wherein, The fourth processing unit adjusts the volume of the first masking sound source using the intensity of the vehicle-related noise and adjusts the volume of the second masking sound source using the intensity of the vehicle-independent noise to generate the third masking sound source.

12. A vehicle noise masking method, comprising: Noise measurement signals are collected from a microphone installed on the vehicle via a communication unit, and the vehicle's driving information is collected via Controller Area Network (CAN) communication. The first processing unit analyzes the composition of the noise measurement signal based on the driving information; The first processing unit classifies the noise measurement signal into vehicle-related noise and vehicle-independent noise. The second processing unit extracts the first masked sound source most similar to the vehicle-related noise from the first sound source group; The third processing unit extracts a second masking sound source from the second sound source group that is similar to the noise unrelated to the vehicle. as well as The third masking sound source is generated by the fourth processing unit using at least one of the first masking sound source and the second masking sound source.

13. The vehicle noise masking method according to claim 12, further comprising: Before extracting the first masked sound source, the sound source is classified into the first sound source group and the second sound source group by the sound source classification unit based on the length, repetition characteristics, overlap characteristics and rhythm characteristics of the sound source, and the classified first sound source group and the second sound source group are stored in the database.

14. The vehicle noise masking method according to claim 12, wherein, The classification of the noise measurement signals includes classifying vehicle-related noise and vehicle-independent noise through correlation analysis between the driving information and the noise measurement signals.

15. The vehicle noise masking method according to claim 12, wherein, Extracting the first masked sound source includes: Analyze the frequency patterns of the vehicle-related noise; Compare the frequency patterns of the first sound source group with those of the vehicle-related noise; and Extract the first masked sound source that is most similar to the vehicle-related noise from the first sound source group.

16. The vehicle noise masking method according to claim 15, wherein, Analyzing the frequency patterns involves analyzing the frequency patterns using the levels of each frequency band and the composition ratios of the vehicle-related noise.

17. The vehicle noise masking method according to claim 12, wherein, Extracting the second masking sound source includes extracting sound sources from the second sound source group that include frequency patterns and impact sound patterns similar to those of noise unrelated to the vehicle as the second masking sound source.

18. The vehicle noise masking method according to claim 17, wherein, Extracting the second masking sound source includes: Analyze the frequency patterns and impact sound patterns of the vehicle-independent noise; Select a sound source that includes a beat tone pattern similar to the impact sound pattern, which is unrelated to the noise of the vehicle; and Select a sound source from the selected sound sources that includes a frequency pattern similar to the frequency pattern of noise unrelated to the vehicle as the second masking sound source.

19. The vehicle noise masking method according to claim 18, wherein, The analysis of the impact sound pattern includes analyzing the generation cycle of noise with an intensity of at least a preset threshold or greater than the preset threshold in the vehicle-independent noise.

20. The vehicle noise masking method according to claim 12, wherein, Generating the third masking sound source includes: Adjust the volume of the first masking sound source using the intensity of the vehicle-related noise; Adjust the volume of the second masking sound source using the intensity of the vehicle-independent noise; and The third masking source is generated using at least one of the first and second masking sources with adjusted volume.

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