Apparatus and method for detecting glass break

• US 6,493,687 B1
• Filed: 12/18/1999
• Issued: 12/10/2002
• Est. Priority Date: 12/18/1999
• Status: Expired due to Fees

• Current Assignee: Detection Systems, Inc. (Robert Bosch GmbH)
• Original Assignee: Detection Systems, Inc. (Robert Bosch GmbH)
• Inventors: DiPoala, William S., Wu, Ji
• Primary Examiner(s): Black, Thomas
• Assistant Examiner(s): Holmes, Michael B.
• Application Number:
  US09/465,254
• Time in Patent Office: 1,088 Days
• Field of Search: 706/15, 706/40, 340/550, 340/541, 340/544, 340/426, 348/88, 367/124, 358/296
• US Class Current: 706/15
• CPC Class Codes: G08B 13/04 by breaking of glass
• CPC Class Codes: G08B 13/1672 using sonic detecting means, e.g. a microphone operating in the audio frequency range
• CPC Class Codes: G08B 29/24 Self-calibration, e.g. compensating for environmental drift or ageing of components

Abstract

A glass break detector is disclosed that uses a neural network to determine if an audio signal is breaking glass. A characteristic extraction unit is used to extract a set of signal characteristics from a time domain signal based on the audio signal. The set of signal characteristics is the set of the magnitudes of the discrete Fourier transform coefficients of an acquired time domain signal, or the Fourier transform coefficients themselves. A classifier is connected to the characteristic extraction unit. It is a two-layer neural network that uses the set of signal characteristics to accurately determine whether the acquired time domain signal represents breaking glass.

Claims

1. An apparatus for detecting breaking glass in an environment, said apparatus comprising:

a sensor unit for acquiring a time domain signal from the environment;

a characteristic extraction unit connected to said sensor for extracting a set of signal characteristics from said time domain signal; and

a classifier connected to said characteristic extraction unit, wherein said set of signal characteristics are used by said classifier as an input data set to determine whether said time domain signal represents breaking glass.

2. The apparatus according to claim 1, wherein the set of signal characteristics are extracted by sampling the time domain signal N times at a rate greater than or equal to a Nyquist rate to thereby create a set of N time domain samples.

3. The apparatus according to claim 2, wherein the set of signal characteristics are extracted by performing a Fourier Transform on the set of N time domain samples to thereby create a set of N frequency domain coefficients.

4. The apparatus according to claim 3, wherein the set of signal characteristics comprises a set of N magnitude values calculated by taking the absolute value of each of the N frequency domain coefficients.

5. The apparatus according to claim 3, wherein the Fourier Transform is a Fast Fourier Transform (FFT).

6. The apparatus according to claim 3, wherein the Fourier Transform is a Discrete Fourier Transform (DFT).

7. The apparatus according to claim 1, wherein the set of signal characteristics comprises a set of magnitudes of N frequency coefficients and the input data set is formed by selecting N/2 magnitudes of said N frequency magnitudes.

8. The apparatus according to claim 7, wherein N=128.

9. The apparatus according to claim 1, wherein the sensor unit further comprises:

a transducer for converting atmospheric waves into the time domain signal, wherein the time domain signal is an analog electrical signal;

an amplifier connected to said transducer for amplifying the time domain signal; and

,

a bandpass filter connected to said amplifier for band-limiting the time domain signal to a predetermined pass-band of frequencies.

10. The apparatus according to claim 9, wherein the predetermined pass-band of frequencies is an approximate range between 20 hz and 1200 hz.

11. The apparatus according to claim 1, further comprising a start condition detection element connected to the sensor unit for detecting a start condition from the time domain signal.

12. The apparatus according to claim 11, wherein the start condition detection unit transmits a start signal to the characteristic extraction unit in response to detecting the start condition.

13. The apparatus according to claim 12, wherein the characteristic extraction unit extracts the set of signal characteristics in response to the start signal.

14. The apparatus according to claim 11, wherein the start condition is a predetermined slope and a predetermined magnitude of the time domain signal.

15. The apparatus according to claim 14, wherein the predetermined slope is approximately 938 volts/second and the predetermined magnitude is approximately 0.5 volts.

16. The apparatus according to claim 1, wherein the classifier comprises a neural network.

17. The apparatus according to claim 16, wherein the neural network comprises a feedforward neural network.

18. The apparatus according to claim 17, wherein the neural network includes an input layer of input neurons, a hidden layer of hidden neurons, and an output neuron, wherein each of said input neurons is connected to each of said hidden neurons by a first set of weighted connections, and each of said hidden neurons is connected to said output neuron by a second set of weighted connections.

19. The apparatus according to claim 18, wherein the set of signal characteristics includes N elements and the input data set includes N/2 elements.

20. The apparatus according to claim 19, wherein the input layer comprises N/2 input neurons for inputting the input data set into the neural network.

21. The apparatus according to claim 18, wherein the hidden layer comprises a number of hidden neurons in an approximate range of between 100 and 140 neurons.

22. The apparatus according to claim 21, wherein an output of each hidden neuron is characterized by a sigmoid function.

23. The apparatus according to claim 18, wherein the output neuron is characterized by a sigmoid function.

24. The apparatus according to claim 18, wherein the neural network is trained to recognize a plurality of collected signal samples as either a glass break signal or a non-glass break signal by a backpropagation algorithm.

25. The apparatus according to claim 24, wherein the backpropagation algorithm trains the neural network to determine whether the time domain signal represents breaking glass by causing the neural network to associate each of the plurality of collected signal samples to a desired neural network output by adjusting the first set of weighted connections and the second set of weighted connections.

26. The apparatus according to claim 25, wherein the plurality of collected signal samples comprises a plurality of glass break sound samples and a plurality of non-glass break sound samples.

27. The apparatus according to claim 1, further comprising a microprocessor, wherein the characteristic extraction unit and the classifier comprise software modules that are executed by said microprocessor.

28. The apparatus according to claim 27, wherein the microprocessor comprises an eight bit floating point processor.

29. The apparatus according to claim 27, wherein the microprocessor comprises a sixteen bit floating point processor.

30. The apparatus according to claim 27, wherein the microprocessor comprises a digital signal processor.

31. A method for detecting breaking glass in an environment, said method comprising the steps of:

acquiring a time domain signal from the environment;

extracting a set of signal characteristics from said time domain signal; and

,

classifying said time domain signal by using said set of signal characteristics as a set of input data to determine whether said time domain signal represents breaking glass.

32. The method according to claim 31, wherein the step of acquiring further comprises the steps of:

converting atmospheric waves into the time domain signal, wherein the time domain signal is an analog electrical signal;

amplifying the time domain signal; and

,

filtering the time domain signal to eliminate frequency components of the time domain signal outside a predetermined pass-band of frequencies.

33. The method according to claim 31, wherein the step of extracting includes sampling the time domain signal N times at a rate greater than or equal to a Nyquist rate to thereby create a set of N time domain samples.

34. The method according to claim 33, wherein the set of signal characteristics are extracted by performing either a Discrete Fourier Transform (DFT) or a Fast Fourier Transform (FFT) on the set of N time domain samples to thereby create a set of N frequency domain coefficients.

35. The method according to claim 34, wherein the set of signal characteristics comprises a set of N magnitude values calculated by taking the absolute value of each of the N frequency domain coefficients.

36. The method according to claim 35, wherein the input data set is formed by selecting N/2 magnitude values of the set of N magnitude values.

37. The method according to claim 31, wherein the step of classifying includes the step of providing a neural network comprising an input layer of input neurons, a hidden layer of hidden neurons, and an output neuron, wherein each of said input neurons is connected to each of said hidden neurons, and said output neuron is connected to each of said hidden neurons in said hidden layer.

38. The method according to claim 37, wherein the step of classifying further comprises the steps of:

multiplying each element of the input data set by an input weight to thereby form a first set of weighted inputs;

summing said first set of weighted inputs to form an input sum for each hidden neuron;

calculating a hidden neuron output using a sigmoid function, wherein said sigmoid function is a function of said input sum;

multiplying each of said hidden neuron outputs by an output weight to thereby form a second set of weighted inputs;

summing said second set of weighted inputs to form a second input sum at an input of the output neuron; and

calculating a classifier output using said sigmoid function, wherein said sigmoid function is a function of said second input sum.

39. A method for fabricating an apparatus for detecting breaking glass in an environment, said method comprising the steps of:

providing a sensor unit for acquiring a time domain signal from the environment;

providing a characteristic extraction unit connected to said sensor for extracting a set of signal characteristics from said time domain signal;

providing a classifier connected to said feature extraction element; and

training said classifier by inputting a plurality of collected signal samples to said classifier and setting an output of said classifier to a desired value, wherein said classifier is trained to determine whether said time domain signal represents breaking glass by learning to associate said plurality of collected signal samples with said desired value.

40. The method according to claim 39, wherein the step of providing a classifier comprises the step of providing a forward feeding neural network having an input layer, a hidden layer connected to said input layer by a first set of weighted connections, and an output layer connected to said hidden layer by a second set of weighted connections.

41. The method according to claim 40, wherein a backpropagation technique trains the neural network to determine whether the time domain signal represents breaking glass by causing the neural network to associate each of the plurality of collected signal samples to a desired neural network output by adjusting the first set of weighted connections and the second set of weighted connections.

42. The method according to claim 41, wherein the plurality of collected signal samples comprises a plurality of glass break sound samples and a plurality of non-glass break sound samples.

43. The method according to claim 39, further comprising the step of providing a microprocessor, wherein the characteristic extraction unit and the classifier comprise software modules that are executed by said microprocessor.