JP2000275096A - Sound-source-kind discrimination apparatus - Google Patents

Abstract

PROBLEM TO BE SOLVED: To obtain a sound-source-kind discrimination apparatus by which the generation of an accident can be detected by discriminating the sound-source- kind of a shock sound precisely. SOLUTION: A traffic sound is collected by a microphone or the like (S101), and whether a shock sound is generated or not is judged by the distribution of its sound pressure (S103). When the shock sound is generated, its power spectrum distribution is computed, and the power spectrum distribution is divided into subbands (S105, S107). Respective spectrum values of the subbands are input to the input layer of a neural network, and a sound-source-kind is judged (S109, S111). On the basis of the relationship between the sound-source-kind and the spectrum values, the neural network is learned (S113) so as to be used in a next judgment.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a sound source type identification device, and more particularly to a sound source type identification device for identifying a sound source of an impact sound generated at an intersection.

[0002]

2. Description of the Related Art In order to ensure traffic safety and reduce accidents, it is essential to prevent a secondary disaster by using an early detection and notification system of the occurrence of an accident and to analyze the mechanism of the occurrence of the accident. In order to analyze the mechanism of the occurrence of an accident, it is necessary to detect accidents and near-miss events, and it is necessary to detect accidents based on traffic sounds such as collision sounds and sudden braking sounds. I have.

When performing such an acoustic analysis,
In general, analysis based on the sound pressure and linear analysis based on the frequency distribution are performed. The analysis based on sound pressure here means that an impact sound is generated when a state where the fluctuation value of the sound pressure exceeds a predetermined threshold value in the time-series sound pressure distribution is observed. It is.

[0004] For example, as a conventional sound source identification method, there is a method in which the frequency of an acoustic signal determined to be an impact sound by sound pressure determination is analyzed, and the sound source of the impact sound is specified from its spectral distribution.

[0005]

When traffic sound is analyzed by these methods and the sound source is specified, a sound exhibiting characteristics similar to the sound of an accident, for example, noise from a cargo bed of a large car or construction work. There are also sounds such as generated metallic sounds in which it is difficult to distinguish the sound source from accident sounds. As a result, erroneous detection of an accident may occur.

For example, a traffic accident automatic recording device (TA
AMS) A Study on the Mechanism of Traffic Accidents ”(Report by the National Police Research Institute, Traffic, Vol. 38, N
o. In the section "(3) Discrimination performance from accident records" in Chapter 6, "Considerations" of Chapter 2, 64-81, 1997, "Step on the lid of an empty can left at an intersection or a gutter on the road with a tire. Sounds and the like have properties similar to accident sounds, it is difficult at present to identify them, and it is not possible to disable metal sounds due to road construction, etc. Actually, TAAMS video recordings In some cases, it is observed that the video is just a large truck passing or recorded even though no special phenomenon has occurred. " It has been reported that the accuracy of accident detection using various analysis methods has certain limitations.

SUMMARY OF THE INVENTION It is an object of the present invention to provide a sound source type identification device capable of more accurately identifying the sound source type of sound.

[0008]

According to one aspect of the present invention, there is provided a sound source type identification apparatus comprising: an input unit for inputting sound; a measuring unit for measuring a sound pressure of the input sound; Calculating means for calculating the power spectrum distribution of the input sound based on the measurement result of the measuring means; dividing means for dividing the calculated power spectrum distribution into a plurality of subbands; Determination means for determining a sound source type of the input sound based on each of the power spectrum distributions,
The determining means has a learning function.

According to the present invention, the sound source type is determined based on each of the power spectrum distributions divided into a plurality of sub-bands by the determining means having a learning function, so that the sound source type can be more accurately identified. It is possible to provide a sound source type identification device that can perform the sound source classification.

Preferably, the calculating means performs the calculation when the sound pressure of the impact sound is measured by the measuring means.

If the calculation is performed when the sound pressure of the impact sound is measured as described above, the sound source type of the impact sound can be determined efficiently.

[0012] Preferably, the judging means is constituted by a neural network.

[0013]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A sound source type identification device according to one embodiment of the present invention will be described below. The sound source type identification device according to the present embodiment employs a sound source identification algorithm based on non-linear association using a neural network or the like instead of linear pattern recognition, and determines the sound source type using the algorithm.

More specifically, the generation of an impact sound is detected by determining the sound pressure, the spectrum distribution is obtained by frequency analysis, and the obtained spectrum distribution is sub-banded into several frequency bands. Then, the sub-banded spectrum distribution is input to the neural network, and the type of the sound source is determined. When such a method is applied, the type of sound source is identified by the neural network, and at the same time, the judgment accuracy can be improved while the device is self-learned in the field.

By constructing an algorithm having such a non-linear association and learning function, it is expected that the accuracy of the determination of the sound source type will be greatly improved.

FIG. 1 is a block diagram showing a configuration of a sound source type identification apparatus according to the present embodiment.

Referring to FIG. 1, a sound source type identification device measures a microphone 101 for collecting traffic sounds near a road (particularly near an intersection, etc.) and a sound pressure of the sound collected by the microphone 101, and calculates and calculates the sound pressure. Pressure calculation circuit 103 for performing a power spectrum calculation, a power spectrum calculation circuit 105 for calculating a power spectrum of sound, a sub-banding calculation circuit 107 for sub-banding the power spectrum into several frequency bands, and determining the type of the sound source It comprises a neural network 109 and a learning circuit 111 for learning the neural network 109 using the output result of the neural network 109.

The neural network 109 outputs a sound source type signal indicating the sound source of the impact sound.

FIG. 2 is a flowchart showing the operation of the sound source type identification apparatus of FIG. Referring to the figure, step S1
At 01, traffic sounds on a road such as an intersection are collected using the microphone 101. Step S103
Then, the sound pressure calculation circuit determines whether or not an impact sound has occurred from the collected time-series sound pressure distribution of the traffic sound. If not, the process returns to step S101, and if so, the process proceeds to step S105.

As shown in FIG. 3, when an impulsive sound is generated due to an accident such as a collision of a vehicle, observing a time-series sound pressure distribution reveals a point where the sound pressure level sharply increases. Therefore, a threshold value is determined in advance, and when the fluctuation value of the sound pressure exceeds the threshold value, it is determined that an impulsive sound has occurred.

If an impulsive sound is generated, step S105
Then, the frequency analysis of the acoustic data at that time is performed, and the power spectrum distribution circuit 105 calculates the power spectrum distribution.

In step S107, the obtained power spectrum distribution is divided into a plurality of sub-bands by the sub-banding operation circuit 107.

FIG. 4 shows a sub-banded power spectrum. For example, subbanding can be performed by averaging power spectra included in a predetermined frequency range.

In step S109, the subbanded spectral values are input to the input layers of the neural network 109, respectively. The neural network is sufficiently intelligent by a teacher signal given by a person in advance, and it is determined in step S111 which type of sound source the impulsive sound is.

For example, referring to FIG. 4, when a sub-banded spectral value is input to the input layer, a pattern appearing in the output layer of the neural network will be described.
(1,0,0, ..., 0,0,0) means vehicle collision sound,
If (0, 1, 0, ..., 0, 0, 0), the sound source is specified by a method such as a sudden braking sound.

In step S113, the determination result (sound source type signal) of the neural network 109 is input to the learning circuit 111, whereby the correspondence between the spectral distribution data and the sound source type is determined by the neural network 109.
Learned in.

After the processing in step S113, the process returns to step S101. The intellectualization of a neural network by a teacher signal given by a person is to associate the spectral distribution data of a plurality of impact sounds collected in advance with the sound source type,
This is to make the neural network learn in advance. In this way, the learning of the impact sound data generally occurring at the intersection is performed.

Further, when the sound source of an impulsive sound is identified in an actual field, the neural network is made to newly learn the data by associating the spectral distribution of the input sound with the identified sound source type. . Such learning can be performed automatically without human intervention.

That is, according to a teacher signal given by a person, (N
-1) When a neural network that has learned a number of impact sound data is used in an actual field, the following processing is performed.

FIG. 5 is a flowchart for explaining a neural network learning method.

Referring to the figure, in step S201, learning using a human teacher signal is performed by a neural network. Thereby, the initialization of the neural network is performed.

In step S205, the sound source data (Nth data) of the impact sound first detected in the field is input to the neural network 109.

In step S207, the neural network 109 identifies the sound source of the Nth data. The correspondence between the determined sound distribution and the sound source type is N
Neural network 1 automatically as the second data
09 learns (S209). In step S211, N
Is incremented by 1 and the process returns to step S205.

Thus, the neural network that has learned the N pieces of data identifies the sound source type of the next detected impact sound ((N + 1) data).

As described above, by successively repeating the identification of the sound source type and the learning, the neural network automatically continues the learning while increasing the intelligence. Thereby,
The sound source type can be identified more accurately.

In the above embodiment, a neutral network is used as a learning system, but a system using chaos or fuzzy having a learning function may be used.

It should be noted that the embodiment disclosed this time is an example in all respects and is not restrictive. The scope of the present invention is defined by the terms of the claims, rather than the description above, and is intended to include any modifications within the scope and meaning equivalent to the terms of the claims.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a configuration of a sound source type identification device according to one of the embodiments of the present invention.

FIG. 2 is a flowchart showing the operation of the apparatus of FIG.

FIG. 3 is a diagram for explaining a method of determining whether or not an impact sound has occurred.

FIG. 4 is a diagram showing sub-banded power spectra and data input to a neural network.

FIG. 5 is a flowchart showing a neural network learning method.

[Explanation of symbols]

 Reference Signs List 101 Microphone 103 Sound pressure operation circuit 105 Power spectrum operation circuit 107 Subband operation circuit 109 Neural network 111 Learning circuit

Claims (3)

[Claims] An input unit for inputting sound; a measuring unit for measuring a sound pressure of the input sound; and a power spectrum distribution of the input sound based on a measurement result of the measuring unit. Calculating means, dividing means for dividing the calculated power spectrum distribution into a plurality of sub-bands, based on each of the power spectrum distributions divided into the plurality of sub-bands, A sound source type identification device, comprising: a determination unit that performs a determination, wherein the determination unit has a learning function. 2. The computer according to claim 1, wherein the calculating unit performs the calculation when the sound pressure of the impact sound is measured by the measuring unit.
2. A sound source type identification device according to item 1. 3. The sound source type identification device according to claim 1, wherein the determination unit is configured by a neural network.