EP4006857A1 - Procédé et dispositif de détection et de classification automatiques des signaux acoustiques - Google Patents

Procédé et dispositif de détection et de classification automatiques des signaux acoustiques Download PDF

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Publication number
EP4006857A1
EP4006857A1 EP21020597.7A EP21020597A EP4006857A1 EP 4006857 A1 EP4006857 A1 EP 4006857A1 EP 21020597 A EP21020597 A EP 21020597A EP 4006857 A1 EP4006857 A1 EP 4006857A1
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Prior art keywords
acoustic
module
classification
sensor system
model
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EP21020597.7A
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German (de)
English (en)
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Volker Schmidt
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B13/00—Burglar, theft or intruder alarms
    • G08B13/16—Actuation by interference with mechanical vibrations in air or other fluid
    • G08B13/1654—Actuation by interference with mechanical vibrations in air or other fluid using passive vibration detection systems
    • G08B13/1672—Actuation by interference with mechanical vibrations in air or other fluid using passive vibration detection systems using sonic detecting means, e.g. a microphone operating in the audio frequency range

Definitions

  • the invention relates to a method and a device for the automatic detection and classification of acoustic signals in a surveillance area to prevent vandalism and crime in objects through to the monitoring and early detection of faults in machines, installations and systems.
  • Monitoring systems for monitoring an open space or an area in a building or building complex comprising a plurality of microphones which can be and/or are arranged in the monitored area and which are designed to record audio signals of an object as an audio signal source in the monitored area known for a long time.
  • a monitoring system which comprises an analysis module for classifying the audio signal and for outputting classification information.
  • the classification information is, in particular, a description of the content of the audio signal and describes the type of noise.
  • the classification information designates the type of origin of the audio signal.
  • an analysis module is provided, which compares an audio signal that is present with a pattern and/or a stored audio signal.
  • the analysis module is designed to classify an audio signal as an unintended noise, namely as a burglary noise and/or as a damage noise and/or as an interference noise of an action of the object as a type of noise.
  • the surveillance system targets the unintended sounds that people either cannot prevent (sounds of footsteps or sounds of an action) or accidentally do not sufficiently prevent.
  • the surveillance system includes a localization module for locating the position of the object by acoustically locating the position of the audio signal source of the classified audio signal in the surveillance area via the microphones and for outputting position information.
  • the localization module is designed to localize the person of the audio signal source of the classified audio signal by acoustic cross-location of the audio signal source, in particular by measuring the transit time of the audio signal from at least two and in particular at least three microphones.
  • the classification information and the position information form part of an object data record, wherein the object data record can be output and/or further processed as a monitoring result.
  • the monitoring system includes an evaluation module for evaluating the object data set and for generating an alarm signal, it also being provided that the evaluation module uses at least two object data sets with different classification information for the evaluation.
  • the evaluation module has a tracking unit, which is designed to create a metadata record for the object from a number of object data records.
  • the evaluation module includes a filter unit for filtering the metadata record, with object data records that contradict a movement model of the object being filtered out.
  • the monitoring system comprises a monitoring center which has an audio storage device or is connected to it, an audio recording of the audio signal, in particular the original audio signal of the classified audio signal, being stored on the audio storage device.
  • a burglar alarm system in which not the audible signal but the inaudible signals are evaluated in order to determine the cause of the noise.
  • filters are used to separate the low-frequency signal (frequency range of around 1-5 Hz) and high-frequency signal (frequency range of around 5-20 Hz) and a comparator compares the signals in the two channels with one another.
  • an acoustic sensor system with a microcomputer for pre-processing the acoustic signals, which is connected to a signal receiver whose output signals are parallel to a module arranged in the acoustic sensor system for their recording and a Module for classification are supplied.
  • a recording database of the acoustic sensor system is connected to the recording module, in which the signal is stored in the format of an audio file.
  • a module for modeling For data exchange and for control with the acoustic sensor system having an interface, a module for modeling is connected, which imports the recordings via an interface, generates corresponding models from them and which is connected via the to a model library of a corresponding sensor system, in which the model selected by a user in a training phase is stored.
  • the classification module is connected to the model library and the classification module is connected to an evaluation application via a further interface, and if a signal is detected, sends the classification result to the evaluation application.
  • the DE 10 2017 012 007 A1 describes a device and a method for universal acoustic testing of objects, the device for acoustic testing of objects having a striking tool, a free-standing microphone and/or a structure-borne microphone for detecting the sound and with an analysis device connected thereto for analyzing the sound up to the ultrasonic range included in.
  • the device is designed as a device which has an excitation device mounted outside of a device housing and an acoustic sensor arranged adjacent thereto, and the output of the acoustic sensor can be connected to either an analysis module or a training module inside the device housing by means of a manually operated operating mode switch, wherein the output of the training module is connected to a model library which is connected to the analysis module.
  • the signals are sent to a central location for recording and for comparison with a reference pattern previously stored in a library, which leads to considerable effort in recording and classification (fixed model-based reference classification or learned reference classification from the previous classifications). leads.
  • recording and classification fixed model-based reference classification or learned reference classification from the previous classifications.
  • the invention is based on the object, based on known methods and devices for the automatic detection and classification of acoustic signals in a surveillance area, to design them in such a way that when configured by the user, all signal-influencing factors are included in the respective detection model and all device-related signals are influencing Factors such as components and assemblies, which are subject to manufacturing tolerances, site-related factors, such as the site-related individually different acoustic background, together with an acoustic signal preserve that is the same for all sensors for each noise to be detected, are included in the respective detection model.
  • At least one acoustic sensor system which has a microcomputer and is connected to an acoustic signal receiver, is provided for preprocessing the acoustic signals for classification, which on the one hand classifies the output signals and compares them with existing acoustic models from the acoustic model library and, in the event of a match, into the acoustic signal recognition database (depending on the type of use of the sensor for further processing and alarming locally in the sensor system itself or stored centrally outside of the sensor system and, on the other hand, carries out a permanent analysis of the stationary acoustic background and saves the detected acoustic deviations in the acoustic signal raw data database, whereupon after a configurable number of such deviations in the acoustic background the module updates the model automatically and without any action being required
  • An acoustic signal model database is updated by the user, after which the module for classification works with one based on the currently prevailing acoustic conditions of the a
  • the output signals of the acoustic signal receiver of the acoustic sensor system are fed to a classification module with an integrated acoustic background recording module, which generates or inserts a new acoustic model in a training phase from these acoustic background signals and from the acoustic signals that are the same for all sensors existing acoustic model is updated and in the classification phase these output signals are classified and compared with already known signal patterns.
  • a corresponding recognition model is automatically generated by the module for modeling model update and made available to the classification module.
  • Each The acoustic sensor system is provided with the same recordings of the acoustic signals to be recognized and enriched with its own recordings of the acoustic background, which differs for each sensor. The resulting models that are generated are then only used for this sensor for classification.
  • a model update cycle runs automatically within the model update module on the basis of data from the classification module in a loop and the acoustic models created by this cycle become the previously known ones by adding the stationary acoustic background deviations formed acoustic background of the respective sensor per sensor system and used by the respective sensor system.
  • An arranged module for modeling model update is connected to the acoustic sensor system.
  • the two modules for classification and model creation/model update can be activated and deactivated from the management application. This makes it possible to use the two operating modes training and classification separately or to operate both in parallel.
  • the acoustic signal to be recognized is recorded beforehand, without any acoustic background signals and at different application-related distances between the sensor and the acoustic signal source, but with the same acoustic signal receiver used later in the sensor and stored in the acoustic signal raw data database.
  • these acoustic signals to be recognized are the same for each sensor of the device described here and do not have to be newly generated and recorded there in each case.
  • the device for performing the automatic detection and classification of acoustic signals according to the method according to the invention comprises at least one acoustic sensor system, a recording database of the acoustic sensor system, interfaces connected to an A/D converter, a hardware module motion detector, a hardware module -GPS, a hardware module video camera, a hardware module wind sensor and a hardware module rain sensor, an acoustic model database, in which previously acoustic models are stored and a classification module with an integrated background signal recording module, which is connected to the acoustic model database and to an evaluation application and, if a signal is detected, the classification result is sent to the evaluation Application sends, so that the acoustic sensor system serves both as a recording system for deviations in the acoustic background for a training phase and as a classification module for known signal patterns during a classification phase and the necessary recognition models from the signals previously recorded by the acoustic sensor system and each individually in each sensor system generates different acoustic background signals and
  • An acoustic sensor system with a microcomputer for pre-processing the acoustic signals compares and, in the event of a match, saves the output signals in the corresponding acoustic signal detection databases of the sensors for further processing and alarming and, on the other hand, carries out a permanent analysis of the stationary acoustic background and saves the acoustic deviations found in the acoustic signal raw data database, whereupon after a configurable number of such deviations in the acoustic background, the module updates the model updates automatically and without any action required by the user, in dess Then the classification module works with the currently prevailing acoustic conditions of the acoustic background and carries out its classifications with this updated acoustic model from now on.
  • An acoustic sensor system with a microcomputer for pre-processing the acoustic signals is stored in the format of an audio file, and is connected to a model building module which imports recordings from the recording database and models them accordingly generated and is connected to an acoustic model database of the corresponding sensor system, in which the acoustic model is stored, and that the classification module is connected to the acoustic model database and, via a further interface, to an evaluation application , and if a signal is detected, sends the classification result to the evaluation application, so that the acoustic sensor system serves both as a recording system for detected acoustic background deviations for a training phase and as a classification module for known signal patterns during a classification phase and the necessary Recognition models are generated from previously recorded signals and acoustic background signals and these models are then only used by this sensor system for classification.
  • the model update module for model creation and updating is connected to the acoustic sensor systems and activates or deactivates both the classification module completely after the model update has taken place in the form of its restart and during an acoustic model update that is being processed with regard to deactivation /Activation of further acoustic recordings of deviations in the acoustic background of the respective sensor during and after the acoustic model update.
  • the sensor system has an amplifier connected to the acoustic signal receiver, whose amplified audio output signal is fed to an analog-to-digital converter and that a microcomputer is connected both to the A/D converter and to at least one interface of the interfaces for data exchange and control of the sensor system is connected via a network.
  • the sensor system has an amplifier connected to the acoustic signal receiver, an analog-to-digital converter and a microcomputer, which can be connected both to the amplifier and analog-to-digital converter via a first interface and to at least one other interface for controlling other connected sensor systems ( Motion detector, GPS, video camera, wind and rain sensor depending on the operating mode) are connected by the sensor system via the network.
  • an amplifier connected to the acoustic signal receiver, an analog-to-digital converter and a microcomputer, which can be connected both to the amplifier and analog-to-digital converter via a first interface and to at least one other interface for controlling other connected sensor systems ( Motion detector, GPS, video camera, wind and rain sensor depending on the operating mode) are connected by the sensor system via the network.
  • Motion detector Motion detector, GPS, video camera, wind and rain sensor depending on the operating mode
  • capacitive converters condenser microphones
  • electrodynamic converters dynamic microphones, pick-ups
  • piezoelectric converters can be used as acoustic signal receivers.
  • the device according to the invention has the advantage that all relevant components for signal processing and classification are located in an intelligent device, the acoustic sensor system.
  • the sensor system serves as a recording system for acoustic backgrounds that are different depending on the location of the sensor and for the recognition acoustic signal recordings previously stored in the sensor system and not to be recorded again for a training phase, as well as a classification module for already modeled and therefore known signal patterns during the classification phase .
  • the model parameters of the underlying recognition model are adapted by the current acoustic input signals.
  • This, but above all the permanent and automatic acoustic model update by the model update module, means that the recognition models are constantly and above all automatically optimized in the course of operation without the user having to do anything.
  • the acoustic sensor thus learns completely independently and thereby enables or improves the quality of the classification under a wide variety of acoustic conditions.
  • a key advantage of the invention compared to the current state of the art is that each acoustic sensor system uses the same detection acoustic signal data and its own recordings regarding the respective different acoustic backgrounds of each sensor system for modeling and this model then only from this device for classification is used. As a result, all factors influencing the signals are included in the respective recognition model and the configuration in the training phase is made possible for the user in a surprisingly simple manner.
  • the permanent and automatic acoustic model update by the model update module means that the recognition models are constantly and above all automatically optimized in the course of operation without the user having to do anything.
  • the acoustic sensor thus learns completely independently and thereby enables or improves the quality of the classification under a wide variety of acoustic conditions.
  • the 1 shows the network structure and an overview of all components of the device according to the invention, the general structure of a network of acoustic sensors according to and an embodiment for carrying out the method according to the invention.
  • the 1 shows an overview of all components of the device according to the invention.
  • the acoustic signal receiver 1, in particular a microphone, receives an acoustic signal and forwards this to an acoustic sensor system S via a connected amplifier and A/D converter 2 .
  • the signal is fed to a classification module 8 for classification.
  • a recording module integrated into the classification module 8 has the task of carrying out a permanent analysis of the stationary acoustic background and storing the acoustic deviations found in the raw acoustic database 10 .
  • An A/D converter 2 a hardware module motion detector 3, a hardware module GPS 4, a hardware module video camera 5, a hardware module wind sensor 6 and a hardware module rain sensor 7 connected to sensor microcomputer S-MC . Furthermore, the sensor microcomputer S-MC is connected to the management application 14 and the hardware module power supply 15 .
  • a central unit Z is also connected to the sensor system S via a computer network 16 .
  • the central unit Z contains a central microcomputer Z -MC , which includes a management application 14, an evaluation application 13 and an acoustics recognition database 17 , with a hardware module power supply 15, a hardware Module wind sensor 6 and a hardware module rain sensor 7 is connected.
  • the classification module 8 is connected to a model library 11 for its task of classifying the input signal.
  • the acoustic model database 11 includes the acoustic models of all known signals. If there is no acoustic model database 11 , the classification module 8 cannot work.
  • To generate an acoustics model database 11 the audio recordings of the raw data target noises previously generated for the sensor system S from the raw acoustics database 10 are used.
  • the module for modeling/model update 9 imports the audio recordings for the target sounds to be recognized from the acoustic raw database 10, generates corresponding acoustic models from them and the corresponding background recordings and places them in the acoustic model database 11 of the corresponding sensor system S.
  • Each sensor system S has its own model update module 9. If the classification module 8 has detected an acoustic signal, it sends the classification result to a management application 14. This application 14 is responsible for further processing of this information and can take appropriate actions , such as B. alarm or control tasks.
  • the two classification modules 8 and model update modules 9 can be activated and deactivated from the management application 14 . This makes it possible to use the two operating modes training and classification separately or to operate both in parallel. Due to the parallel operation of both modules, it is possible in the classification phase to also record deviating and previously unknown background signals to the system, which can later be used to improve existing acoustic models in the acoustic model database 11 with regard to the expand detection.
  • the lower part of 1 shows the general structure of the centralization and networking of acoustic sensor systems S.
  • the sensor system S which is connected to the acoustic signal receiver 1 , whose output signals are fed to a classification module 8 , which on the one hand classifies the output signals and existing acoustic models from the acoustic model database 11 and, if they match, stored in the acoustic recognition database 12 and/or 17 depending on the operating mode for further processing and alarming, and on the other hand a permanent analysis of the stationary acoustic background carries out and stores the detected acoustic deviations in the raw acoustic database 10 .
  • the acoustic detection database 17 via the computer network 16 .
  • the basis for networking a sensor domain is a classic local computer network LAN 16 or the wireless variant WLAN, both modules are integrated components of the microcomputer S-MC.
  • Another interface built into the microcomputer is S4. It is used to output video signals to a video camera or to an HDMI device such as a B. for connecting a monitor for configuration directly on the sensor system S.
  • the external components evaluation application 13 and management application 14 use standard network protocols for data exchange and for controlling the acoustic sensor systems S using the microcomputer S-MC, which meets the requirements for networking via LAN or WLAN provides. Entire sensor domains can be realized with this type of networking. In the domains, the individual sensor systems S work completely independently, but can be configured and controlled using classic network functions.
  • the recognition models are generated from acoustic raw data signals that are previously recorded independently of the sensor and are the same for all sensor systems S. From these, together with the individually location-dependent, different background recordings, a corresponding recognition model is then automatically generated in each sensor system S and made available to the classification module 8 .
  • the analysis and further recording of deviating background acoustic data in the classification module 8 is deactivated until the new/updated acoustic model is provided by the model update module 9 . This prevents further background recordings, because these are most likely already included in the current model update.
  • a management application 14 is available for the administration and management of the sensor systems S.
  • the user can View or change the configuration of each sensor system S , as well as the programs and services running on the sensor system S. It is also possible to view the status of the existing acoustic models from here.
  • the configuration of each sensor system S is stored in a separate area and table in the acoustic model database 11 .
  • the management application 14 is used via a bot account of a messenger provider, for example the provider Telegram or Whatsapp, which has to be created beforehand. Both provider bot api's are supported by the sensor system S.
  • a project is understood to mean a set consisting of 1 to n intelligent sensor systems S.
  • the sensor systems S are managed within projects. All sensor systems S are created and administered in the acoustic detection database 12 and/or 17 in MySQL database tables, for example the “Sensor” table, depending on the mode of operation of the sensor.
  • a customer ID and location ID assigned to each sensor system S there assigns each sensor to a customer and a location.
  • This is only one at a time, with networks the number can theoretically be n.
  • Each sensor system S must be entered in the acoustic detection database 12 and/or 17 , depending on the operating mode of the sensor -> single-sensor operation or -> multi-sensor operation), otherwise no database action takes place, e.g. E.g. alarm in the event of a positive detection of an acoustic signal.
  • the entries are made using the MySQL database interface.
  • the Hidden Markov Model (HMMI) on which the known invention is based and used for generating acoustic signal models is deliberately not used by this invention, since the Hidden Markov Model (HMMI) generates acoustic signal models in addition to all recognizing acoustic signal models of the target noises to be recognized, the acoustic background of the sensor system S must also be stored as a recognition acoustic model and the system then makes a selection from all these acoustic models when classifying/recognizing an acoustic signal . This presupposes that, in particular in the acoustic background model, all variants of possible acoustic backgrounds must be observed in order to be able to make an almost error-free selection in the classification / recognition.
  • the entire classification in the present invention was implemented using a proprietary classification solution, which exclusively uses the acoustic signals to be classified according to the acoustic modeling for the classification and the individually location-dependent acoustic background of each sensor only for the change analysis of the background in the acoustic model database 11 .
  • An individual model is created for each sensor system S , based on which the sensor system S then works. This requires one or more corresponding training phase(s) in order to also individually configure the sensor system S and adapt it to the place of use. This is done fully automatically by the classification module 8 and the model update module 9 on the basis of the configuration data stored by the user.
  • this value is chosen to be very small, e.g. 1, whereby the type of modeling of the acoustic signals to be recognized already ensures that no acoustic signal to be recognized is classified as an unknown background segment.
  • This threshold represents an additional safeguard to prevent this.
  • each sensor system S contains an integrated microcomputer S-MC , the sensor systems S are coupled to one another and to the management 14 and evaluation applications 13 via a computer network (LAN) 16 or a wireless network (WLAN). This type of coupling is technically very mature and can be implemented economically.
  • LAN computer network
  • WLAN wireless network
  • the acoustic signal receiver 1, z. B. microphone or sound transducer are connected directly to the sensor system S or are mechanically (e.g. with a gooseneck) and connected by audio cables in the immediate vicinity. Furthermore, it is possible to arrange the acoustic signal receiver 1 in the housing of the intelligent sensor system S in such a way that it can be adjusted vertically and laterally towards the monitored area. It is equally possible to provide the acoustic signal receiver 1 for outdoor use with a wind shield in order to reduce or completely avoid clipping or noise caused by wind.
  • the signal processing in the acoustic sensor system S is largely independent of the acoustic signal receiver 1 used.
  • the acoustic models generated differ from one another depending on the acoustic signal receiver 1 used and its mechanical installation in a housing (eg windbreak). For this reason, the data for the sensor system S in the raw acoustic database 10 must always have been generated with the same acoustic signal receiver 1 and its mechanical structure.
  • the invention described here Due to a permanent acoustic model update, automatically triggered by a changed acoustic background, the invention described here has real practicability, both indoors and outdoors for objects and systems to be monitored.

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  • Engineering & Computer Science (AREA)
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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)
EP21020597.7A 2020-11-28 2021-11-26 Procédé et dispositif de détection et de classification automatiques des signaux acoustiques Withdrawn EP4006857A1 (fr)

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DE102020007273.9A DE102020007273A1 (de) 2020-11-28 2020-11-28 Verfahren und Vorrichtung zur automatischen Erkennung und Klassifizierung von akustischen Signalen

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009104968A1 (fr) 2008-02-22 2009-08-27 Idteq As Système de détection d'intrusion à reconnaissance de signaux
DE102012211154A1 (de) 2012-06-28 2014-01-02 Robert Bosch Gmbh Überwachungssystem, Freiflächenüberwachung sowie Verfahren zur Überwachung eines Überwachungsbereichs
DE102014012184B4 (de) 2014-08-20 2018-03-08 HST High Soft Tech GmbH Vorrichtung und Verfahren zur automatischen Erkennung und Klassifizierung von akustischen Signalen in einem Überwachungsbereich
DE102017012007A1 (de) 2017-12-22 2019-06-27 HST High Soft Tech GmbH Vorrichtung und Verfahren zur universellen akustischen Prüfung von Objekten
WO2019159103A1 (fr) * 2018-02-15 2019-08-22 Tyco Fire & Security Gmbh Système de détection de tir doué d'une modélisation et d'une surveillance de bruit ambiant

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009104968A1 (fr) 2008-02-22 2009-08-27 Idteq As Système de détection d'intrusion à reconnaissance de signaux
DE102012211154A1 (de) 2012-06-28 2014-01-02 Robert Bosch Gmbh Überwachungssystem, Freiflächenüberwachung sowie Verfahren zur Überwachung eines Überwachungsbereichs
DE102014012184B4 (de) 2014-08-20 2018-03-08 HST High Soft Tech GmbH Vorrichtung und Verfahren zur automatischen Erkennung und Klassifizierung von akustischen Signalen in einem Überwachungsbereich
DE102017012007A1 (de) 2017-12-22 2019-06-27 HST High Soft Tech GmbH Vorrichtung und Verfahren zur universellen akustischen Prüfung von Objekten
WO2019159103A1 (fr) * 2018-02-15 2019-08-22 Tyco Fire & Security Gmbh Système de détection de tir doué d'une modélisation et d'une surveillance de bruit ambiant

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