JP2009264806A - Device, method and program for detecting strange sound - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a device, a method and a program for detecting a strange sound which are contrived for detecting the strange sound from equipment such as a transformer. <P>SOLUTION: The device for detecting the strange sound includes a means for converting into digital signals a first signal detected by a first microphone installed at a position apart at a prescribed distance from a monitoring object and a second signal detected by a second microphone installed at a position apart at a prescribed length from the first microphone, a means for removing a steady sound of the monitoring object and producing first and second steady sound removed digital signals, a significant sound detecting means which determines the presence of the steady sound removed digital signal which includes an absolute value exceeding a prescribed threshold and showing continuity, a means which performs a periodic signal component removing processing for removing a periodic signal component of a prescribed level or above of a significant sound signal when a significant sound is determined to be present, so as to produce first and second digital signals for comparison, calculates the correlation coefficients of these signals, shifting them by a unit time, and specifies a unit-time shifted value having the largest value of the correlation coefficient, and a means which determines whether the sound from the monitoring object is the strange one from the unit-time shifted value. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a technique for detecting an abnormal sound that may be emitted from an object to be monitored.

  A technique for detecting abnormal noise in facilities such as nuclear reactors is disclosed in, for example, Japanese Patent Application Laid-Open No. 57-114846. Specifically, in an abnormal sound detection device that detects an abnormal sound such as an impact sound by installing acoustic detectors at a plurality of different locations, the difference in time at which the output signal of each acoustic detector is detected, A means for generating an alarm only when the time difference is within a preset range is provided. However, the acoustic detector is attached to the monitoring object and cannot be used for all the monitoring objects.

  Japanese Patent Application Laid-Open No. 2004-85455 discloses an abnormal sound source search technique that can easily and accurately specify an abnormal sound source. Specifically, the sound waveform collection unit of the processing unit collects the current sound waveform when the device is operating from three or more microphones arranged at different positions in the sound source search area A. The comparison unit compares the reference sound waveform stored in the reference sound waveform storage unit in advance with the current sound waveform, extracts only the abnormal sound waveform, and the residual information extraction unit excludes only the amplitude information from the abnormal sound waveform. Then, residual information from which noise due to attenuation of sound pressure generated during propagation of abnormal sound is eliminated is extracted. The time difference calculation unit obtains a time difference until the abnormal sound from the same sound source reaches each microphone by cross-correlating the residual information collected by the two microphones, and based on the time difference obtained by the sound source specifying unit, Specify the sound source position. Since the abnormal sound waveform is detected by comparison with a reference sound waveform prepared in advance, a problem occurs when the stationary sound fluctuates in frequency.

Furthermore, Japanese Patent Application Laid-Open No. 2003-111183 discloses a technique for enabling a noise source such as a factory noise to be accurately identified and displayed even outdoors with a simple configuration. . Specifically, the microphones M1 to M4 are arranged so that the detection unit forms a square centered on the origin O in the XY plane, and the fifth microphone M5 is configured by the detection unit from the microphones M1 to M4. Is located above the center of the square, and arranged so that the distance between the microphone M5 and the microphones M1 to M4 is equal, and the direction of the sound source is estimated from the arrival time difference between the output signals of the microphones M1 to M5. A video in the vicinity of the estimated sound source position is collected by a camera, and the estimated sound source position is displayed on the video displayed on the display of a personal computer. However, this is not a configuration for detecting abnormal sounds.
Japanese Patent Application Laid-Open No. 57-114846 JP 2004-85455 A JP 2003-111183 A

  As described above, the conventional technology does not disclose a configuration suitable for detecting abnormal noise from equipment such as a transformer.

  Accordingly, an object of the present invention is to provide a technique for detecting abnormal noise from equipment such as a transformer.

  The abnormal sound detection apparatus according to the present invention converts a first signal detected by a first microphone installed at a position away from a monitoring object by a predetermined distance into a first digital signal, further from the monitoring object. Conversion means for converting a second signal detected by a second microphone installed at a position away from the first microphone by a predetermined length into a second digital signal, and sound generated constantly by the monitoring object Means for processing the first and second digital signals with a filter set to remove the frequency of the first and second stationary sound-removed digital signals, and first and second stationary sounds The removal digital signal is analyzed to determine whether there is a stationary sound removal digital signal whose absolute value of the signal value continues for a predetermined period and exceeds a predetermined threshold value. Steadily sound removal Significant sound detection means for identifying a digital signal as a significant sound signal, and periodic signal component removal processing for removing periodic signal components of a predetermined level or more of the significant sound signal when the significant sound signal is identified by the significant sound detection means Is applied to the first and second stationary sound elimination digital signals to generate first and second comparison digital signals, and the correlation coefficient of the first and second comparison digital signals is set to the first. And a second difference digital signal that is calculated by shifting each of the second comparison digital signals by unit time, a time difference specifying unit that specifies a unit time shift value having the largest correlation coefficient value, and a unit time specified by the time difference specifying unit And means for determining whether or not the noise is from the monitoring object based on the deviation value.

  Unlike conventional techniques, a stationary sound such as a transformer is removed using a filter, so that it is possible to cope with some fluctuations of the stationary sound without any problem. In addition, by performing the periodic signal component removal processing, it is possible to remove a signal component that is a problem in the correlation coefficient calculation, and thus it is possible to accurately calculate the correlation coefficient. Furthermore, it can be determined from the unit time deviation value described above whether the sound is an abnormal sound from the monitored object or an external sound.

  In addition, the above-described filter may be a high-pass filter that does not cause a phase shift. If no phase shift occurs, the unit time shift value can be accurately calculated.

  Furthermore, in the periodic signal component removal process described above, a band rejection filter that blocks the frequency at which the highest power is detected in the frequency spectrum of the significant sound signal may be configured and used. In this way, the correlation coefficient can be calculated more accurately.

  The abnormal sound detection apparatus can be composed of a computer and a program for causing the computer to execute the above-described processing, such as a flexible disk, a CD-ROM, a magneto-optical disk, a semiconductor memory, and a hard disk. Stored in a storage medium or a storage device. Moreover, it may be distributed as a digital signal via a network or the like. The intermediate processing result is temporarily stored in a storage device such as a main memory.

  According to the present invention, it is possible to detect abnormal noise from equipment such as a transformer.

  The positional relationship between the abnormal sound detection device and the monitoring object according to an embodiment of the present invention will be described with reference to FIG. For example, the monitoring object 200 such as a transformer and the microphones 1 and 2 of the abnormal sound detection apparatus 100 according to the present embodiment are arranged close to each other so as to be aligned. That is, the straight line A passing through the microphones 1 and 2 and the side surface or the tangent to the side surface of the monitoring target 200 on the abnormal sound detection device 100 side are arranged to be perpendicular.

  The distance L1 between the microphone 1 closer to the monitoring object 200 and the monitoring object 200 is, for example, about 20 cm, and the distance L2 between the microphone 1 and the microphone 2 is, for example, about 5 cm, but is not limited thereto. Is not to be done. The distance L1 may be a distance that can detect an abnormal sound from the monitoring target 200, and the distance L2 may be a distance that causes a time difference that can distinguish the external sound from the abnormal sound.

  Next, the functional block diagram of the abnormal sound detection apparatus 100 is shown using FIG. The abnormal sound detection apparatus 100 includes microphones 1 and 2, an A / D conversion unit 111 connected to the microphones 1 and 2 and including an amplifier and other circuits, and sound data connected to the A / D conversion unit 111. Storage unit 113, stationary sound removal processing unit 115 that performs stationary sound removal processing on the sound data from microphone 1 and the sound data from microphone 2 stored in sound data storage unit 113, and stationary sound removal processing The filtering data storage unit 117 that stores the processing result of the unit 115, the significant sound detection unit 119 that performs the significant sound detection process on the data stored in the filtering data storage unit 117, and the significant sound detection unit 119 When a sound is detected, data from the microphone 1 and data from the microphone 2 are stored using the data stored in the filtering data storage unit 117. A time difference detection unit 121 that calculates a time difference between significant sounds, a filter setting unit 123 that calculates a coefficient value of a band rejection filter in response to a request from the time difference detection unit 121, and outputs the coefficient value to the time difference detection unit 121, and a time difference detection A time difference data storage unit 125 that stores the processing result of the unit 121, and an abnormal sound detection that determines whether or not an abnormal sound is detected based on the data stored in the time difference data storage unit 125 and outputs a warning when detecting the abnormal sound Part 127.

  With the microphones 1 and 2, sound generated from the monitoring object 200 and other external sounds are converted into electric signals, and further converted into digital signals by the A / D conversion unit 111. The digital signal generated by the A / D conversion unit 111 is stored in the sound data storage unit 113 as digital data. In the present application, digital data is also referred to as a digital signal. For the data from the microphone 1 and the data from the microphone 2 stored in the sound data storage unit 113, the steady sound removal processing unit 115 removes the steady sound emitted from the monitoring object 200. Filtering processing is performed, and the processing result is stored in the filtering data storage unit 117. The significant sound detection unit 119 sequentially determines the data from the microphone 1 and the data from the microphone 2 stored in the filtering data storage unit 117, and the absolute value of the data value exceeds a predetermined threshold value. In the time slot (length L) set from the time when the value exceeds the predetermined threshold, if there is a data value that exceeds the predetermined threshold for two consecutive slots, it is determined that significant sound is detected, Data that satisfies the condition (data from the microphone 1 or data from the microphone 2) is specified as a significant sound signal.

  When the significant sound detection unit 119 determines that significant sound is detected and a significant sound signal is designated, the time difference detection unit 121 includes the significant sound signal included in the first time slot set by the significant sound detection unit 119. Frequency analysis is performed on the data of the other side, and the peak and peak frequency of the power spectrum are specified. When the peak of the power spectrum exceeds a predetermined level, the filter setting unit 123 is requested to calculate the coefficient value of the band rejection filter that removes the peak frequency. The filter setting unit 123 calculates a coefficient value of a band rejection filter that removes the peak frequency by a known method, and outputs the coefficient value to the time difference detection unit 121. The time difference detection unit 121 forms a band rejection filter that removes the peak frequency, and processes the data from the microphone 1 and the data from the microphone 2 stored in the filtering data storage unit 117. When the peak of the power spectrum is equal to or lower than a predetermined level, the process proceeds to the following process without configuring the band rejection filter as described above. Further, the time difference detection unit 121 shifts data that is not a significant sound signal for the head length J in the first time slot by unit time (sampling time), and calculates a correlation coefficient for the significant sound signal data. . Then, the unit time lag value with the maximum correlation coefficient is specified as time difference data, and stored in the time difference data storage unit 125 together with the designation of the significant sound signal.

  The abnormal sound detection unit 127 determines whether the sound is abnormal sound or foreign sound by comparing the time difference data stored in the time difference data storage unit 125 with a threshold value. (Or wired) to notify the external administrator, or output the occurrence of abnormality by other output means.

  Next, specific processing contents of the abnormal sound detection apparatus 100 will be described with reference to FIGS. 3 to 10. First, the A / D conversion unit 111 converts the electric signal corresponding to the sound generated by the monitoring target 200 and other external sounds from the microphone 1 and the microphone 2 into a digital signal, and stores the digital signal as sound data. The data is stored in the unit 113 (step S1). Since the frequency of A / D conversion affects the resolution of time difference detection performed below, if the distance between the microphone 1 and the microphone 2 is about 5 cm, at least 44.1 kHz is required.

  Next, the stationary sound removal processing unit 115 removes the stationary sound that is constantly output from the monitoring target 200 for each of the data from the microphone 1 and the data from the microphone 2 stored in the sound data storage unit 113. In order to do so, filtering by a high-pass filter is performed, and the filtered data is stored in the filtering data storage unit 117 (step S5). For example, FIG. 4A shows an example of a waveform of a stationary sound in the time axis direction when the monitoring target 200 is a transformer, and FIG. 4B shows a power spectrum of each frequency. From FIG. 4A (the vertical axis represents amplitude and the horizontal axis represents time), a sound having a periodic waveform is output, and the power is applied to a low frequency band of several hundred Hz or less from FIG. (The vertical axis represents dB and the horizontal axis represents frequency). Therefore, in the present embodiment, the high pass filter is configured by an FIR filter. Since the FIR filter does not cause a phase shift, it is possible to prevent the time difference detection performed below from being affected. For example, a high pass filter as shown in FIG. 5 is employed. In FIG. 5, the horizontal axis represents frequency and the vertical axis represents gain. In this way, in the example of FIG. 5, a signal having a frequency of about 200 Hz or more is allowed to pass, but a frequency lower than that is removed.

Specifically, the following convolution operation is performed. Sa1 [n] represents the value of the signal from the microphone 1, and Sa2 [n] represents the value of the signal from the microphone 2. Hk represents a kth coefficient value (k = 0 to n) when a high-pass filter is configured by an FIR filter. It is well known to construct a high pass filter with an FIR filter and will not be discussed further here. Further, Sb1 [n] represents the value of the signal from the microphone 1 after the high-pass filter, and Sb2 [n] represents the value of the signal from the microphone 2 after the high-pass filter. n represents a sampling number.

  For example, waveforms of digital signals detected when applause is given from the outside are shown in FIGS. 6A shows the waveform of a digital signal from the microphone 1, for example, and FIG. 6B shows the waveform of a digital signal from the microphone 2, for example. On the other hand, the waveform of the digital signal after the high-pass filter is shown in FIGS. 7 (a) and 7 (b). FIG. 7A shows the waveform of the digital signal after filtering for the microphone 1, for example. FIG. 7B shows the waveform of the digital signal after filtering for the microphone 2, for example. Comparing FIGS. 6 (a) and 6 (b) with FIGS. 7 (a) and 7 (b), it can be seen that the stationary sound portion from the transformer has been removed.

  Steps S1 and S3 are continuously performed while signals from the microphones 1 and 2 are input.

  Next, the significant sound detection unit 119 performs a significant sound detection process using the data stored in the filtering data storage unit 117 (step S5). In this step, when there is a change in the signal value, it is determined whether it is noise or significant sound. Specifically, as shown in FIG. 8, the time slot TS of length L is set in the time axis direction from the time when the absolute value of Sb1 [n] or the absolute value of Sb2 [n] exceeds the threshold Th. To do. In the example of FIG. 8, only three time slots are set, but two or more time slots may be set. Since the signal value exceeding the threshold Th is detected in the first time slot TS1, the significant sound detection unit 119 determines whether the signal value exceeding the threshold Th is further detected in the second time slot TS2. If a signal value exceeding the threshold Th is detected even in the second time slot TS2, it is determined that significant sound is detected. If no signal value exceeding the threshold Th is detected even in the second time slot TS2, only noise is detected. It is judged that. This is to maintain a signal value such that the significant sound continues to some extent and exceeds the threshold value.

  If the significant sound detection unit 119 determines that noise is detected in the processing as described above (step S7: No route), the significant sound detection unit 119 returns to step S5 and performs significant sound detection processing on subsequent data.

  Once the signal value exceeding the threshold value Th is detected, the significant sound detection unit 119 regards it as a series of significant sounds as long as the time slot TS in which the signal value exceeding the threshold value Th continues, and during that time, the significant sound is detected. Do not perform the detection process. That is, the significant sound detection process is performed after the time slot when the signal value is equal to or less than the threshold Th again.

  On the other hand, when the significant sound detection unit 119 determines that significant sound is detected in the processing described above, the significant sound detection unit 119 notifies the time difference detection unit 121 of significant sound detection and is significant from either the microphone 1 or the microphone 2. Data (microphone identifier) indicating whether the sound was detected first is output. Since the setting of the time slot TS is also used in the following processing, the setting data of the time slot TS (such as the start time of each time slot) is stored in the filtering data storage unit 117.

  Once the signal value exceeding the threshold value Th is detected, the significant sound detection unit 119 regards it as a series of significant sounds as long as the time slot TS in which the signal value exceeding the threshold value Th continues, and during that time, the significant sound is detected. Do not perform the detection process. That is, the significant sound detection process is performed after the time slot when the signal value is equal to or less than the threshold Th again.

  For the time slot in which significant sound is detected for the signal from one microphone, the processing is stopped because it is not necessary to perform significant sound detection processing for the signal from the other microphone.

  Next, the time difference detection unit 121 adversely affects the calculation of the correlation coefficient described below when the significant sound contains a strong signal component with periodicity. A component is included, and if included, the filter setting unit 123 is requested to calculate a coefficient value for constructing a band rejection filter for removing the periodic signal component, and is calculated. The filtering process using the obtained coefficient values is performed and stored in a storage device such as a main memory (step S9). In addition, when a signal component having periodicity above a predetermined level is not included, there is no need for the configuration of the band rejection filter and the filtering process.

Specifically, the complex coefficient of the discrete Fourier transform of Sb1 [n] (here, the significant sound is detected from the microphone 1 first, but the processing content itself is the same in the case of the microphone 2). Sf1 [f] is calculated. For this calculation, FFT (Finite Fourier transform) can be generally used. Note that n ranges from 0 to L-1, but this means that the operation is performed in the range of the time slot TS1.

When the peak of the power spectrum | Sf1 [f] | ² exceeds the threshold value Tp, a band rejection filter that removes the signal component p is configured.

That is, when f = 0 to L / 2-1 and f giving the maximum value of | Sf1 [f] | ² is p, it is expressed as follows.

Therefore, when | Sf1 [p] | ² > Tp, the filter setting unit 123 is requested to calculate the coefficient value of the band rejection filter that cuts the frequency p. The filter setting unit 123 calculates a coefficient value b _k of a band rejection filter that cuts the frequency p by performing a known calculation process, and outputs the coefficient value b _k to the time difference detection unit 121.

Then, the filtering process is performed by the following convolution calculation.

However, when | Sf1 [p] | ² ≦ Tp, the following setting is made.
Sc1 [n] = Sb1 [n]
Sc2 [n] = Sb2 [n]

  For example, FIGS. 9A and 9B show data examples when the above processing is completed. 9A and 9B, the horizontal axis represents time, and the vertical axis represents amplitude. FIG. 9A shows the processed signal waveform for the microphone 1, and FIG. 9B shows the processed signal waveform for the microphone 2. The vertical line in the center represents the timing when the significant sound exceeds the threshold Th. As described above, in FIG. 9, the processed signal for the microphone 1 is detected as early as 7 samples in the processed signal for the microphone 2.

  The time difference detection unit 121 calculates a correlation coefficient between the processed signal for the microphone 1 and the processed signal for the microphone 2 with respect to a predetermined range of sample deviation x, and the sample having the maximum correlation coefficient is calculated. The deviation D is specified and stored in the time difference data storage unit 125 (step S11).

なお、比較の対象は、タイムスロットＴＳ１の先頭部分の長さＪ（例えば５０サンプル程度）の部分である。そして、ｘをプラスマイナスの所定の範囲内で変更してそれぞれについてＲ［ｘ］を算出する。 Specifically, after the processing of step S9, the signal value for the microphone 2 is shifted by the x sampling time, and the correlation coefficient R between the signal value for the microphone 2 and the signal value for the microphone 1 shifted by the x sampling time. [X] is calculated.

Note that the comparison target is the length J (for example, about 50 samples) of the head portion of the time slot TS1. Then, x is changed within a predetermined range of plus or minus, and R [x] is calculated for each.

When x giving the maximum value of R [x] is a sample deviation D, it is expressed as follows.

  D is a value representing a time difference when the significant sound reaches the microphone 1 and the microphone 2, that is, a delay of the microphone 2 with respect to the microphone 1. The time difference detection unit 121 stores the sample deviation D in the time difference data storage unit 125.

  When the correlation coefficient is calculated for each sample deviation x for the signals shown in FIGS. 9A and 9B, the result is as shown in FIG. In FIG. 10, the vertical axis represents the value of the correlation coefficient, and the horizontal axis represents the sample deviation value. It can be seen that a peak exists in the seventh sample from FIG.

Then, the abnormal sound detection unit 127 determines whether D ≧ T _D (predetermined threshold) (step S15). When this condition is met, the abnormal sound detection unit 127 outputs a warning to the administrator by wired or wireless communication because of abnormal noise detection (step S19). Note that the warning may include the ID of the monitoring target 200 to facilitate location identification.

  If it is determined in step S15 that the condition is not satisfied, it is an external sound, so there is no need to output a warning. Accordingly, the process proceeds to step S19.

  If it is determined in step S15 that the condition is not satisfied, or if the process is not terminated after step S19 (step S21: No route), the significant data section detected in step S7 in the filtering data storage unit 11 The processing target is skipped to the data after the tail of (step S23), and the process moves to the next significant sound detection. That is, the process returns to step S5. On the other hand, when the process is to be terminated due to an instruction to terminate the process (step S21: Yes route), the process is terminated.

  Note that FIG. 3 shows a flow from step S19 to step S5 via step S23, but steps S1 to S7 and steps S9 to S19 may be performed asynchronously. That is, the significant sound detection unit 119 sequentially processes the data stored in the filtering data storage unit 117 during the period from step S9 to step S19, and detects the last significant sound section detected once. However, when the tail end is detected, the significant sound detection process described above may be switched to and executed.

  By performing the processing as described above, even if the steady sound from the monitoring target 200 fluctuates somewhat, it can be processed without any problem as long as it is within the band cut by the high-pass filter. In addition, any monitoring object 200 can be dealt with by setting a filter that cuts a stationary sound. Furthermore, although the correlation coefficient in the time axis direction is calculated, in order to adversely affect the calculation of this correlation coefficient, a band rejection filter is used to remove frequency components whose peaks exceed the threshold in the power spectrum. Thus, the correlation coefficient can be calculated accurately, whereby the time difference between the two detection signals can be specified with high accuracy. That is, it can be determined whether the sound is abnormal or foreign.

  Although the embodiment of the present invention has been described above, the present invention is not limited to this. For example, the functional block diagram shown in FIG. 2 is an example, and may not necessarily match the actual program module configuration.

  In the above example, when the correlation coefficient is calculated, the signal of the microphone 1 is fixed and processed. However, the signal of the microphone 2 may be fixed. In such a case, since the sign of D is inverted, it is also necessary to invert the sign in step S15.

  Furthermore, a plurality of abnormal noise detection devices may be provided, and the presence or absence of abnormal noise may be determined by combining the detection results. For example, in FIG. 1, there may be a case where the noise detection device is arranged on the side opposite to the side where the noise detection device 100 is arranged.

  The abnormal sound detection device 100 is a computer device. In the computer device, as shown in FIG. 11, a memory 2501 (storage unit), a CPU 2503 (processing unit), a hard disk drive (HDD) 2505, and a display device 2509 are provided. A display control unit 2507 connected to the computer, a drive device 2513 for a removable disk 2511, an input device 2515, and a communication control unit 2517 for connecting to a network are connected by a bus 2519. Application programs including an operating system (OS) and a Web browser are stored in the HDD 2505, and are read from the HDD 2505 to the memory 2501 when executed by the CPU 2503. If necessary, the CPU 2503 controls the display control unit 2507, the communication control unit 2517, and the drive device 2513 to perform necessary operations. Further, data in the middle of processing is stored in the memory 2501 and stored in the HDD 2505 if necessary. Such a computer realizes various functions as described above by organically cooperating hardware such as the CPU 2503 and the memory 2501 described above with the OS and necessary application programs. The computer apparatus may be configured by a plurality of computers. Note that the A / D converter 111 may be obtained as a separate device, or may be realized by a part of components connected to the bus 2519.

It is a figure which shows the example of arrangement | positioning of the apparatus which concerns on embodiment of this invention. It is a functional block diagram of the abnormal sound detection apparatus which concerns on embodiment of this invention. It is a figure which shows the processing flow in embodiment of this invention. (A) And (b) is a figure which shows an example of the stationary sound of the monitoring target object. It is a figure which shows an example of the high pass filter for removing a stationary sound. (A) And (b) is a figure which shows an example of the detection sound containing an external sound. (A) And (b) is a figure which shows an example of the sound wave after removing a stationary sound. It is a figure for demonstrating a time slot. (A) And (b) is a figure showing the signal waveform after the filtering process by a band rejection filter. It is a figure showing the relationship between a correlation coefficient and sample deviation. It is a functional block diagram of a computer.

Explanation of symbols

DESCRIPTION OF SYMBOLS 100 Abnormal sound detection apparatus 200 Monitoring target object 111 A / D conversion part 113 Sound data storage part 115 Stationary sound removal process part 117 Filtering data storage part 119 Significant sound detection part 121 Time difference detection part 123 Filter setting part 125 Time difference data storage part 127 Abnormal sound detector

Claims (5)

A first signal detected by a first microphone installed at a predetermined distance from the monitoring object is converted into a first digital signal, further away from the monitoring object and predetermined from the first microphone. Conversion means for converting a second signal detected by a second microphone installed at a long distance into a second digital signal;
The first and second digital signals are generated by processing the first and second digital signals with a filter set so as to remove the frequency of the sound that is constantly emitted by the monitoring object. Means to
The first and second stationary sound removal digital signals are analyzed to determine whether there is a stationary sound removal digital signal in which the absolute value of the signal value continues for a predetermined period and exceeds a predetermined threshold value. Significant sound detection means for identifying the stationary sound removal digital signal whose determination has become affirmative first as a significant sound signal,
When the significant sound signal is specified by the significant sound detection means, a periodic signal component removal process for removing a periodic signal component having a predetermined level or higher of the significant sound signal is performed. The first and second comparison digital signals are generated on the signal, and the correlation coefficient of the first and second comparison digital signals is set to any one of the first and second comparison digital signals. A time difference specifying means for calculating the unit time deviation value by shifting the unit time by unit time and specifying the unit time deviation value at which the value of the correlation coefficient is the largest,
Means for determining whether or not the monitoring object is an abnormal sound based on the unit time lag value specified by the time difference specifying means;
An allophone detecting device. The abnormal noise detection apparatus according to claim 1, wherein the filter is a high-pass filter that does not cause a phase shift. In the periodic signal component removal process,
The abnormal sound detection device according to claim 1, wherein a band rejection filter that blocks a frequency at which the highest power is detected in a frequency spectrum of the significant sound signal is configured and used. A first signal detected by a first microphone installed at a predetermined distance from the monitoring object is converted into a first digital signal, further away from the monitoring object and predetermined from the first microphone. Converting a second signal detected by a second microphone placed at a long distance into a second digital signal;
The first and second digital signals are generated by processing the first and second digital signals with a filter set so as to remove the frequency of the sound that is constantly emitted by the monitoring object. And steps to
The first and second stationary sound removal digital signals are analyzed to determine whether there is a stationary sound removal digital signal in which the absolute value of the signal value continues for a predetermined period and exceeds a predetermined threshold value. A significant sound detection step for identifying as a significant sound signal a stationary sound removal digital signal whose determination has become affirmative first,
When the significant sound signal is specified in the significant sound detection step, a periodic signal component removal process for removing a periodic signal component having a predetermined level or higher of the significant sound signal is performed. The first and second comparison digital signals are generated on the signal, and the correlation coefficient of the first and second comparison digital signals is set to any one of the first and second comparison digital signals. A time difference specifying step for calculating the unit time shift value by shifting the unit time by unit time and specifying the unit time shift value at which the value of the correlation coefficient is the largest,
Determining whether or not the monitoring object is an abnormal sound based on the maximum value of the unit time deviation value specified in the time difference specifying step;
An abnormal sound detection method executed by a computer. A first digital signal generated from a first signal detected by a first microphone installed at a predetermined distance from the monitored object, and further away from the monitored object and from the first microphone. The second digital signal generated from the second signal detected by the second microphone placed at a predetermined distance away from the second digital signal is removed from the frequency of the sound that the monitoring object steadily emits. Processing with a set filter to generate the first and second stationary sound removal digital signals;
The first and second stationary sound removal digital signals are analyzed to determine whether there is a stationary sound removal digital signal in which the absolute value of the signal value continues for a predetermined period and exceeds a predetermined threshold value. A significant sound detection step for identifying as a significant sound signal a stationary sound removal digital signal whose determination has become affirmative first,
When the significant sound signal is specified in the significant sound detection step, a periodic signal component removal process for removing a periodic signal component having a predetermined level or higher of the significant sound signal is performed. The first and second comparison digital signals are generated on the signal, and the correlation coefficient of the first and second comparison digital signals is set to any one of the first and second comparison digital signals. A time difference specifying step for calculating the unit time shift value by shifting the unit time by unit time and specifying the unit time shift value at which the value of the correlation coefficient is the largest,
Determining whether or not the monitoring object is an abnormal sound based on the maximum value of the unit time deviation value specified in the time difference specifying step;
An abnormal sound detection program for causing a computer to execute.

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