KR20210064018A - Acoustic event detection method based on deep learning - Google Patents

Abstract

Disclosed is a method that extracts a complex feature value included in the personal sound data based on the sound data including at least one sound source, classifies at least one or more sound source included in the complex feature value using an artificial intelligence model based on a fast region-CNN-LSTM, and detects an event using the classified sound source. Therefore, the present invention is capable of providing improved real-time detection based on sound information as well as situation detection using image information.

Description

Acoustic event detection method in deep learning-based detection situation {ACOUSTIC EVENT DETECTION METHOD BASED ON DEEP LEARNING}

The present disclosure relates to a method and system for detecting an acoustic event in a deep learning-based sensing situation, and the present disclosure provides a method and system for detecting an acoustic signal with different characteristics by using a fast regional convolution-based network (FR-CNN) to detect the frequency domain of an acoustic signal with different characteristics. , it is possible to detect complex sounds in real time regardless of distance.

A technology for detecting an acoustic event and classifying the type of acoustic event has been continuously studied in order to be applied to a user's judgment of the surrounding environment by being fused with a context-aware technology.

Since the conventional single sound sensing model according to this study only detects a sound that is clearly visible, there is a problem in that information is damaged when actually sensing a complex sound source occurring at the same time. That is, there was a problem in that the accuracy of detecting a complex sound source was low.

In addition, the conventional acoustic event detection system has a problem in that the accuracy of the detection environment is lowered when noise greater than the sound source to be detected is detected depending on the environment, and the accuracy of the sound source detection is lowered according to the distance from the point where the sound source is generated. There was a problem.

An object of the present disclosure is to train an artificial intelligence model using a fast regional convolution based network (F-R-CNN), and to detect real-time acoustic events in a detection situation.

An object of the present disclosure is to detect a complex acoustic event that extracts acoustic features regardless of distance and detects a plurality of acoustic occurrences in an acoustic sensing situation.

The present disclosure provides a method for detecting an event using sound data, the method comprising: receiving sound data including at least one sound source; extracting a composite acoustic feature included in the acoustic data; classifying each of at least one sound source included in the composite sound feature using an artificial intelligence model; and detecting an event based on a combination of each of the classified sound sources.

In addition, in the present disclosure, the step of classifying the sound source included in the composite acoustic feature by using the artificial intelligence model includes obtaining a convolutional feature map of the static feature, and a preset region of interest from the convolutional feature map ( Classifying the sound source by extracting a feature vector of a region of interest), obtaining a feature map of the differential feature, extracting a feature vector from the feature map of the differential feature, and classifying the sound source included in the feature vector may include

The present disclosure may provide improved real-time detection based on sound information as well as situation detection using image information using a fast regional convolution based network (F-R-CNN).

In addition, since the present disclosure identifies the acoustic characteristics regardless of the distance, it is possible to compensate for the disadvantages of the existing acoustic-based sensing system and to detect the occurrence of a plurality of sounds to accurately detect the occurrence of acoustic events.

1 shows a flowchart according to an embodiment of the present disclosure.
2 illustrates an algorithm progress according to an embodiment of the present disclosure.
3 illustrates an algorithm progress according to an embodiment of the present disclosure.
4 shows Attention-LSTM according to an embodiment of the present disclosure.
5 illustrates a Connectionist Temporal Classification (CTC) according to an embodiment of the present disclosure.
6 shows performance according to test results of the present disclosure.
7 shows performance according to test results of the present disclosure.
8 shows performance according to test results of the present disclosure.

Hereinafter, exemplary embodiments disclosed in the present specification will be described in detail with reference to the accompanying drawings, but identical or similar elements are denoted by the same reference numerals regardless of reference numerals, and redundant descriptions thereof will be omitted.

The accompanying drawings are only for easy understanding of the embodiments disclosed in the present specification, and the technical spirit disclosed in the present specification is not limited by the accompanying drawings, and all changes and equivalents included in the spirit and scope of the present disclosure It should be understood to include water or substitutes.

Terms including ordinal numbers such as first and second may be used to describe various elements, but the elements are not limited by the terms. The above terms are used only for the purpose of distinguishing one component from another component.

When the expression of the term used in the present disclosure is defined, it is as follows.

Acoustic event detection (AED), regional-convolutional neural networks (R-CNN)), long short-term memory (LSTM), connectionist temporal classification (CTC).

The present disclosure proposes an acoustic event detection system that operates in a variety of noisy environments and distances. The method uses a regional convolution-based network (R-CNN) to detect the frequency domain of time-series acoustic events with different characteristics in a noisy environment. At this time, by using a fast regional convolution based network (F-R-CNN), the present disclosure can be implemented in real time.

This enables semi-supervised learning without manually labeling the region of interest information in the frequency band.

In addition, according to the present disclosure, the input value of the artificial intelligence model may be designed to use a multi-feature value in order to solve the problem that the event detection performance depends on the signal strength according to the distance.

Additionally, a long-short-term memory neural network was applied for the time-series characteristics of the signal, and the sound source detection performance was improved by reflecting the length characteristics of the sound source through the attention algorithm. Finally, one event including a complex sound source is detected by applying connectionist temporal classification (CTC).

Hereinafter, FIG. 1 will be described.

1 shows a flowchart of the present disclosure.

Referring to FIG. 1 , the acoustic event detection system of the present disclosure may include receiving acoustic data including at least one sound source ( S10 ).

Specifically, the sound source may include a source of a sound, and the acoustic event detection system may include sound data including at least one sound source.

For example, one sound data may include all sounds generated from a specific object or living things, such as a dialogue sound source, a sound source that an object collides with, and a gunshot. And the sound data may include sound characteristic data extracted from the sound source.

At this time, in order to obtain accurate acoustic data, noise removal may be performed in the preprocessing process, and if the spectral frequency band of noise is different from the event in which it occurred or if each event has a unique frequency band, R-CNN (region-convolutional neural networks) can be used.

The acoustic event detection system of the present disclosure may include extracting a complex feature value included in the acoustic data (S20).

In this case, the composite feature value may include a static feature that is a spectrogram of the received sound data and a differential feature based on a difference between the sound data and sound data before a preset time. have.

In more detail, the static feature may mean a log Mel-Band energy-based image including the sound data feature.

In this case, the Log Mel-Band energy is energy that can well represent the characteristics of the acoustic signal. However, there is a limitation in analyzing complex acoustic signals. Acoustic data is generally normalized by the power of the data, but sometimes, the characteristic information may be damaged if the intensity is weakened according to distance or mixed with noise.

Therefore, in the present disclosure, considering that the acoustic data has a time series characteristic, it is possible to measure the change in log mel band energy through the difference from the previous data in the current acoustic data.

Since the differential feature generated through the above method is based on the amount of change between the current sound data and the spectrum in which the sound data before a preset time is expressed as an image, the characteristics of the sound data are determined regardless of the distance from the point of generation of the sound data. can reflect

After acquiring the complex feature value, the present disclosure may classify at least one sound source included in the complex feature value using an artificial intelligence model (S30). And it is possible to detect an event based on the combination of each of the classified sound sources (S40).

Hereinafter, the algorithm of the present disclosure will be described in detail with reference to FIGS. 2 to 5 .

The present disclosure discloses a Fast R-CNN-LSTM based real-time sensing system using region information for various types of deep learning output layers.

Specifically, according to the present disclosure, a fast R-CNN-Attention LSTM (Fast R-CNN-Attention LSTM) is trained using training data in which acoustic data is expressed as an image to generate an FFast R-CNN-LSTM (31) model. can

The present disclosure selects training data in which the decoded value of the generated FFast R-CNN-LSTM (31) model has a score greater than or equal to a predetermined reference value, and uses the selected training data to perform semi-supervised region labeling. Through the SFast R-CNN-LSTM (32) model can be generated. The SFast R-CNN-LSTM 32 model generated by the above process can minimize the hand labeling cost because the model can extract the region representing the characteristics of the sound source in the spectrum section by itself.

It will be described in detail below with reference to FIG. 2 .

In general, CNN (Convolution Neural Network) is an artificial intelligence model suitable for image processing. In the present disclosure, multiple viewpoints can be collected to use acoustic data as an input of CNN, and multiple frames of acoustic data can be configured into one image for use in image processing.

And the artificial intelligence model of the present disclosure may include an event detection algorithm according to the acoustic structure that connects CNN and RNN (LSTM).

Referring to FIG. 2 , the algorithm of the present disclosure may include an FFast R-CNN-LSTM 31 model, an SFast R-CNN-LSTM model 32 , and an artificial intelligence model 33 in which differential features are reflected.

At this time, the FFast R-CNN-LSTM 31 may be a pre-trained model using unlabeled training data in the full-band height of the existing Fast R-CNN-LSTM. have.

Specifically, the fast regional convolutional neural network (Fast R-CNN) refers to a method proposed to improve the shortcomings of the bottleneck structure of R-CNN, in which all of the Bounding Box Proposals are generated at the locations where feature data exists from the image. have. Specifically, compared to the previous R-CNN, each Bbox-Proposal does not go through the CNN, but rather performs object detection at the output feature map stage after passing through the CNN once for the entire image.

The present disclosure may also include a SFast R-CNN-LSTM model 32 .

In this case, the SFast R-CNN-LSTM (32) model can extract a region representing the characteristics of the sound source in the spectrum section by itself.

Specifically, the present disclosure may select appropriate training data obtained by obtaining a score greater than or equal to a preset value as a result of a test of the FFast R-CNN-LSTM in order to train the SFast R-CNN-LSTM model 32 . In addition, the SFast R-CNN-LSTM model 32 of the present disclosure may include Fast R-CNN-LSTM using semi-supervised region labeling. Since the SFast R-CNN-LSTM model 32 of the present disclosure uses selected training data, it is possible to minimize the hand labeling cost.

And the sensing system of the present disclosure may include an artificial intelligence model 33 in which differential features are reflected.

In this case, the artificial intelligence model 33 reflecting the differential feature may be trained in parallel with the SFast R-CNN-LSTM model 32 . In addition, one event detection can be performed by sharing some layers for acoustic detection with the SFast R-CNN-LSTM model 32 .

In this case, some layers may include a CTC algorithm layer.

The artificial intelligence model 33 in which the differential feature is reflected includes a layer in which CNN and LSTM are connected, and when the differential feature is provided to an algorithm in which CNN and RNN (LSTM) are connected, a feature vector reflecting the differential feature is generated. can At this time, the differential feature includes the amount of change obtained by subtracting the image information of the sound data before a specific time (T-1 time) from the image information of the sound data at a specific time (T time) extracted from the sound source, and the artificial intelligence model ( 33) can be learned using the differential feature value.

3 will be described below.

Referring to FIG. 3 , the process of the sound sensing system of the present disclosure is shown.

The sound sensing system of the present disclosure may receive sound data including at least one sound source and extract a complex feature value included in the sound data (S20). And a static feature among the extracted complex feature values is input to the FFast R-CNN-LSTM model 31, and the output result of the FFast R-CNN-LSTM model 31 is a preset interest set in step S20. The region RoI may be reset.

The sensing system of the present disclosure provides a static feature of acoustic data to the SFast R-CNN-LSTM model 32, extracts a reconfigured feature vector of a region of interest from a convolutional feature map of static features of the acoustic data, and extracts the composite feature You can classify the sound source included in the value.

In addition, the artificial intelligence model 33 in which differential features are reflected in parallel with the SFast R-CNN-LSTM model 32 extracts a feature vector from the convolutional feature map using the differential feature of the acoustic data, , it is possible to classify the sound source included in the complex feature value.

At this time, the artificial intelligence model 33 reflecting the differential feature and the SFast R-CNN-LSTM model 32 reflecting the static feature share a specific layer to receive at least one sound source included in the complex feature value, respectively. It may include a step of classifying (S30) and a step of detecting an event based on a combination of each of the classified sound sources (S40).

Specifically, a specific shared layer may include an Attention-LSTM algorithm and a Connectionist Temporal Classification (CTC) algorithm in which time-series and regional features are reflected.

At this time, the Attention-LSTM algorithm and the CTC algorithm are included in each of the models 31, 32, and 33 shown in FIG. 2 to perform sound sensing.

Hereinafter, Attention-LSTM and CTC algorithms will be described with reference to FIGS. 4 to 5 .

4 shows an Attention algorithm of the present disclosure.

First, the acoustic data obtained in the event detection of the present disclosure should be able to detect events of various lengths.

The present disclosure extracts regional features of sound data from a feature map and applies an attention algorithm to assign specific weights to the regional features to increase sound detection accuracy. The attention algorithm can be trained by unsupervised learning that does not require labeling.

5 shows a CTC (Connectionist Temporal Classification)-based post processing process.

The sensing system of the present disclosure may detect a final event by labeling each time-step or frame wisely of the acoustic data using CTC (Connectionist Temporal Classification).

For example, if 3 frames of sound data are received and the sound source classification result for each frame is determined to be i) a car tire sound, ii) a vehicle impact noise, and iii) a human sound, the detection system detects the obtained sound data of 3 frames. 'Traffic Accident Occurrence' can be output as the final event using In this case, Connectionist Temporal Classification (CTC) may be used as a method of determining the final event.

6 to 8 show performance results according to tests of the present disclosure.

6 shows a comparison result according to post-processing of the present disclosure. Referring to FIG. 6 , it can be seen that the F1 score is higher after the post-processing according to the presence or absence of the post-processing.

In addition, it can be seen that the F1 score is the highest when complex feature values are used and the Attention algorithm is used.

7 to 8 show statistical values indicating that the performance of the algorithm of the present disclosure shows the highest performance when a composite feature value including a static feature and a differential feature is used.

In particular, it can be seen that the model using the differential feature performs much better in a noisy environment than the static feature.

The present disclosure described above can be implemented as computer-readable code on a medium in which a program is recorded. The computer-readable medium includes all types of recording devices that store data that can be read by a computer system. Examples of computer-readable media include hard disk drives (HDDs), solid state disks (SSDs), silicon disk drives (SDDs), ROMs, RAM, CD-ROMs, magnetic tapes, floppy disks, optical data storage devices, etc. There is this. In addition, the computer may include a processor of the terminal.

Claims (9)

In the event detection method using acoustic data
Receiving sound data including at least one sound source;
extracting a complex feature value included in the sound data;
classifying at least one sound source included in the complex feature value, respectively, using an artificial intelligence model; and
Including the step of detecting an event based on each combination of the classified sound sources,
How to detect an event. The method of claim 1,
The composite feature value is
Including a static feature that is a spectrogram of the received sound data and a differential feature based on a difference between the sound data and sound data before a preset time,
How to detect an event. According to claim 2
The artificial intelligence model,
FFast R-CNN-LSTM, in which a fast regional convolution long-short-term neural network (Fast R-CNN-LSTM) was pre-trained using unlabeled training data at full-band height, and the FFast R-CNN-LSTM training Including SFast R-CNN-LSTM trained by selecting data and an artificial intelligence model that reflects differential features,
How to detect an event. 4. The method of claim 3
Classifying the sound source included in the complex feature value using the artificial intelligence model includes:
Obtaining the convolutional feature map of the static feature using the FFast R-CNN-LSTM, and classifying the sound source by extracting a feature vector of a preset region of interest from the convolutional feature map doing,
How to detect an event. 5. The method of claim 4
The step of classifying a sound source by extracting a feature vector of a preset region of interest from the convolutional feature map,
resetting the region of interest based on a feature vector of a preset region of interest output by the FFast R-CNN-LSTM; and
The SFast R-CNN-LSTM further comprising extracting a feature vector of a reset ROI from the convolutional feature map of the static feature to classify the sound source included in the complex feature value,
How to detect an event. The method of claim 5,
The step of detecting an event based on each combination of the classified sound sources,
Using CTC (Connectionist Temporal Classification) to label each time-step or frame of the acoustic data to detect a final event,
How to detect an event. 4. The method of claim 3
Classifying the sound source included in the complex feature value using the artificial intelligence model includes:
Acquiring the feature map of the differential feature by the artificial intelligence model reflecting the differential feature, extracting a feature vector from the feature map of the differential feature, and classifying the sound source included in the feature vector,
How to detect an event. The method of claim 7,
The step of detecting an event based on each combination of the classified sound sources,
Using CTC (Connectionist Temporal Classification) to label each time-step or frame of the acoustic data to detect a final event,
How to detect an event. 4. The method of claim 3
Classifying the sound source included in the complex feature value using the artificial intelligence model includes:
The FFast R-CNN-LSTM obtains the convolutional feature map of the static feature, extracts a feature vector of a preset region of interest from the convolutional feature map, and resets the preset region of interest step;
obtaining, by the SFast R-CNN-LSTM, a convolutional feature map of the static feature, and extracting a feature vector of the reset ROI;
obtaining a feature map of the differential feature by an artificial intelligence model to which the differential feature is reflected, and extracting a feature vector from the feature map of the differential feature;
classifying the sound source included in the feature vector; and
Using CTC (Connectionist Temporal Classification) to label each time-step or frame of the acoustic data to detect a final event,
How to detect an event.

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