KR20210115425A - Smart Volume Control System of Voice Information System According to Noise Levels by Real-Time Acoustic Analysis - Google Patents

Abstract

The present invention relates to a smart volume control system (1). The smart volume control system (1) comprises: an input unit (3) for collecting ambient sound; an extraction unit (5) which analyzes the sound input in real time through the input unit (3) and simultaneously extracts sound and noise data; a voice analysis unit (7) for dividing and analyzing the extracted sound data by time; a learning unit (9) which learns the sound data extracted through the extraction unit (5) through deep learning, extracts and vectorizes a sound pattern, analyzes similarity, and distinguishes whether the analyzed sound pattern is general noise or accident noise; an IOT linkage unit (11) which notifies an external organization by interworking with an IOT device in accordance with the result learned by the learning unit (9); a noise control unit (13) for classifying the sound analyzed by the voice analysis unit (7) in accordance with intensity; and an output unit (17) for performing announcements in accordance with the analysis result of the sound analysis unit (7) and the noise control unit (13). Therefore, the present invention can appropriately control the output volume of a voice guidance system in accordance with the ambient noise level.

Description

Smart Volume Control System of Voice Information System According to Noise Levels by Real-Time Acoustic Analysis

The present invention relates to a smart volume control system, and more particularly, by extracting sounds made by people, vehicles, etc. from sound sources collected in real time, and analyzing the extracted sound data to classify the noise level by hour, day, daytime and nighttime And it relates to a technology that can properly adjust the output volume of the voice guidance system according to the ambient noise level by classifying sound patterns by deep learning.

Recently, with the development of smart home and smart city services to which IoT technology is applied, a lot of things such as bus arrival time guidance at bus stops, crosswalks or roads, voice guidance of pedestrian safety systems, and voice guidance devices for reducing fine dust are being installed. .

However, such a conventional voice guidance device has the following problems.

First, due to a uniform device that is always output with a constant sound, there is an inconvenience due to the voice guidance being too loud during the time of heavy traffic and not being heard during the time of heavy traffic.

Second, there is a problem in that it is difficult to properly respond to the situation because it is not possible to distinguish whether the noise is general noise or noise caused by an accident.

Patent Application No. 10-2019-0098376 (Title: Intelligent voice output method, voice output device and intelligent computing device)

Accordingly, the present invention has been devised to solve such a problem, and an object of the present invention is to provide a system capable of accurately delivering a message to be delivered by variably broadcasting according to ambient noise during announcement.

In addition, another object of the present invention is to extract the sounds made by people, vehicles, etc. from the sound sources collected in real time, and classify the noise level by hour, day, day and night by analyzing the extracted sound data, and by deep learning It is to provide a system that can provide appropriate guidance according to the situation by classifying the acoustic pattern.

In addition, another object of the present invention is to provide a system capable of quickly responding to an accident by distinguishing whether the ambient noise is a simple noise or an accident-induced noise.

In order to achieve the above object, an embodiment of the present invention,

an input unit 3 for collecting ambient sound;

an extraction unit 5 that analyzes the sound input in real time through the input unit 3 and simultaneously extracts sound and noise data;

a voice analyzer 7 for dividing and analyzing the extracted sound data by time;

a learning unit 9 that learns the sound data extracted through the extraction unit 5 through deep learning, extracts sound patterns, vectorizes them, analyzes the similarity, and distinguishes whether the analyzed sound patterns are general noise or accident noise;

an IOT interworking unit 11 that interworks with the IOT device and informs an external organization according to the result learned by the learning unit 9;

a noise control unit 13 for classifying the sound analyzed by the voice analysis unit 7 according to intensity; and

It provides a smart volume control system (1) including an output unit (17) for performing a guide broadcasting according to the analysis results of the sound analysis unit (7) and the noise control unit (13).

As described above, the smart voice guidance system according to an embodiment of the present invention has the following effects.

First, by analyzing the surrounding noise level from the sound received in real time using the microphone or decibel measuring sensor of the input unit, when the noise level is high, the volume of the output unit is increased to faithfully function as a voice guidance system, When the level of this quiet noise level is low, the net function of the voice guidance system can be improved by reducing the volume of the output unit to reduce the noise caused by voice guidance to people around it and performing its original function.

Second, by separating general noise from noise generated during an accident, the sound of emergency situations such as violence or car crashes are analyzed, and through the IoT device interlocked with the system, the nearest police station or sound quality rescue center, etc. Alerting information can save people from emergencies.

Third, by additionally disposing a cough sound recognition unit, it is possible to analyze the cough sound among the collected acoustic data, and if there is a pedestrian coughing at the corresponding stop, it can be identified in real time and notified to the relevant organization, and also the corresponding pedestrian can be broadcasted to the relevant organization. It has the advantage of guiding you to visit.

1 is a diagram schematically showing the structure of a smart volume system according to an embodiment of the present invention.
FIG. 2 is a block diagram schematically illustrating a structure of a learning unit (CPU) illustrated in FIG. 1 .
FIG. 3 is a block diagram schematically showing the structure of the sound extraction unit shown in FIG. 1 .
FIG. 4 is a block diagram schematically illustrating the structure of an Internet of things (IOT) interworking unit shown in FIG. 1 .
FIG. 5 is a view showing sound waves processed by the smart volume system shown in FIG. 1 .
6 is a block diagram schematically showing the structure of a cough sound recognition unit as another embodiment of the present invention.

Hereinafter, a smart volume control system according to the present invention will be described in detail with reference to the accompanying drawings.

1 to 5 , the smart volume control system 1 proposed by the present invention extracts a human sound, a vehicle sound, etc. from real-time input sound, and simultaneously analyzes the extracted sound data and noise It relates to a system (1) that controls and outputs the volume of the voice guidance system (1) by analyzing it.

This smart volume control system (1) is,

an input unit 3 for collecting ambient sound; an extraction unit 5 that analyzes the sound input in real time through the input unit 3 and simultaneously extracts sound and noise data; a voice analyzer 7 for dividing and analyzing the extracted sound data by time; a learning unit 9 that learns the sound data extracted through the extraction unit 5 through deep learning, extracts sound patterns, vectorizes them, analyzes the similarity, and distinguishes whether the analyzed sound patterns are general noise or accident noise; an IOT interworking unit 11 that interworks with the IOT device and informs an external organization according to the result learned by the learning unit 9; a noise control unit 13 for classifying the sound analyzed by the voice analysis unit 7 according to intensity; And it includes an output unit 17 for performing the announcement according to the analysis results of the sound analysis unit 7 and the noise control unit 13.

In more detail,

The input unit 3 is an electronic device that collects ambient sound, and includes, for example, an embedded microphone or a decibel measuring sensor. Accordingly, ambient sound is collected through the input unit 3 , and the collected sound is converted into digital data and transmitted to the extraction unit 5 .

The extraction unit 5 separates sound data and noise data from the transmitted sound.

The extraction unit 5 separates the sound data and the noise data by band-filtering the sound signal and passing only a specific frequency.

The extraction unit 5 includes an input terminal 20 to which a sound wave signal transmitted from the input unit 3 is input, as shown in FIG. 3 ; an amplifier 22 for amplifying the digital signal output from the input terminal 20; a band filter 24 for passing only frequencies of a specific band among the amplified signals; a detector (Detector; 26) for detecting the filtered specific frequency; and a shaper 28 for shaping the signal output from the detector 26 .

In the extraction unit 5 having such a structure, the acoustic signal passing through the input terminal 20 may be amplified to a predetermined frequency or higher while passing through the amplifier 22 .

In addition, the amplified signal may output only a frequency of a specific band by the band filter 24 . That is, the band filter 24 has an arrangement structure in which an integrating circuit and a differentiating circuit are combined by a resistor, a plurality of capacitors, and a diode.

And, the boundary frequency of the first capacitor and the second capacitor may be determined by the following equation. in other words,

The boundary frequency of the first capacitor: f1=1/2*π*R*C1 -------- Equation 1

The boundary frequency of the second capacitor: f2=1/2*π*R*C2 -------- Equation 2

(f1: first boundary frequency, f2: second boundary frequency, R: resistance, C1, C2: first and second capacitors)

As can be seen from Equations 1 and 2, the first boundary frequency and the second boundary frequency can be set by appropriately varying the capacitance of the capacitor or resistor.

Accordingly, when the target frequency f satisfies the condition of f1<f<f2, that is, only frequencies corresponding to the middle bands of the first and second boundary frequency bands may pass.

In this way, noise data can be separated by setting a reference frequency that separates noise data and sound data.

For example, a signal in a frequency band of 20 Hz to 100 Hz is set as sound data, and a signal in a frequency band of 100 Hz to 200 Hz is set as noise data.

In some cases, by disposing a plurality of band filters 24 and making the pass frequency band of each band filter 24 into multiple channels, it is possible to separate noise data according to more various criteria.

Then, the extracted acoustic data is analyzed for a characteristic pattern for each time by the voice analysis unit 7 .

That is, the extracted sound data is stored by dividing it into hourly, daily, daytime and nighttime categories. For example, by combining the time data with the sound data, it is possible to determine the collection time of each sound data.

This may combine time data with sound data by interworking with the voice analyzer 7 and a digital timer.

By combining the time data in this way, the sound data can be stored separately by time.

Then, the sound data is analyzed by the learning unit 7 to extract a characteristic pattern, and vectorized. That is, it can be vectorized by the learning unit 9 and analyzed in a deep learning manner.

In more detail, the learning unit 9 classifies the sound pattern by analyzing the sound pattern by deep learning to distinguish the noise from the general sound. The learning unit 9 may be processed by a central processing unit (CPU) equipped with an acoustic analysis engine.

The learning unit 9 includes: a central processing unit 10 for classifying the sound according to the degree of similarity by classifying the sound data by a deep learning method; and a sound classification module 30 for classifying the sound data classified by the central processing unit 10 as normal noise or accident noise.

The central processing unit 10, as shown in FIG. 2, is composed of an acoustic analysis engine, a GPU, an ISP (Image Signal Processor), ALSA (Advanced Linux Sound Architecture), RTSP (Real Time Streaming Protocol), and a RAM (RAM), LAN or Beacon.

In the central processing unit 10, various algorithms can be used to analyze the pattern of sound, for example, by comparing frequency bands, waveforms, etc. of sound by a deep learning method and determining the similarity of the pattern. method to analyze.

In other words, deep learning is a machine learning method built on the basis of artificial neural networks (ANNs) to enable computers to learn on their own like humans using multiple data.

Since classification and clustering of acoustic data are possible using an artificial neural network, similarity determination can be performed by placing various layers on data desired for classification or clustering.

That is, the degree of similarity can be determined by vectorizing acoustic data with an artificial neural network, extracting waveform features, and using the features as input values for other machine learning algorithms to classify or cluster for each waveform.

The artificial neural network includes a deep neural network, and the deep neural network refers to a neural network composed of several layers among neural network algorithms.

That is, the artificial neural network is composed of multiple layers, and each layer is composed of several nodes, and an operation that actually classifies the waveform of the acoustic data occurs at each node. designed to mimic the process.

When a node receives a stimulus of a certain size or more, it responds, and the magnitude of the response is roughly proportional to the value multiplied by the input value and the node's coefficients (or weights, weights). In general, a node receives several sound data and has a coefficient equal to the number of inputs. Therefore, different weights can be given to different input values by adjusting this coefficient.

Finally, the multiplied values are all added and the sum is fed into the activation function. The result of the activation function corresponds to the output of the node, and this output is ultimately used for classification or regression analysis.

Each layer consists of several nodes, and whether each node is activated/deactivated is determined according to the input of sound data. At this time, the input data becomes the input of the first layer, and after that, the output of each layer becomes the input of the next layer again.

All coefficients change little by little in the process of learning the waveform of the acoustic data, and as a result, reflect which input each node considers important. And training of the neural network is the process of updating this coefficient.

When learning the waveform of acoustic data, in such a deep neural network, different layer features are learned for each layer.

That is, simple and specific features are learned for low-level features (eg, a curved shape (C) constituting a waveform of acoustic data), and more complex and abstract features are learned for high-level features. {Example: Waveform Height (H), Spacing (R), Curvature (P)}

Through this abstraction learning process, the deep neural network understands high-dimensional acoustic data, and hundreds of millions of coefficients are involved in this process. (A non-linear function is used in this process.)

In addition, deep neural networks can use the data to identify the latent structures of the data. That is, potential structures such as the height (H) of the waveform of the acoustic data, the interval (R), and the curvature (P) of the peak can be grasped. Through this, similarity between data can be effectively identified even if the data is not labeled, and as a result, deep neural networks are effective for clustering acoustic data.

For example, a large amount of acoustic data may be input using a neural network, and similar acoustic data may be collected and classified.

And, even when learning the unlabeled data, the neural network can automatically extract the characteristics of the acoustic data. There are several methods for this automatic extraction, and in general, this process learns to make the output equal to the input when passed through the neural network.

No matter what kind of label is (use the input as is/use a separate label), the neural network finds the correlation between the input and the output. In some cases, after training the neural network to some extent with labeled data, it is possible to continue learning by adding unlabeled data. Using this method, the performance of the neural network can be maximized.

The last layer of a deep neural network is the output layer. In most cases, the activation function of the output layer is logistic or softmax, and the probability of a specific label can be finally obtained from the output layer. For example, when sound data is input, whether the shape of the waveform is short and dense, long and smooth, etc. can be obtained with respective probabilities.

First, all coefficients of the neural net are initialized before training starts. Then, the learning proceeds by repeatedly inputting sound data. If the learning proceeds smoothly, the coefficients will be updated to appropriate values, and various classifications and predictions are possible with this artificial neural network.

In the learning process, the updating process of these coefficients occurs repeatedly.

The principle of coefficient update is to estimate the coefficient first, measure the error that occurs when the coefficient is used, and then slightly update the coefficient based on the error.

At this time, the multiple coefficients of the neural network are collectively called a model, and the model may be in an initialized state or in a state in which learning is completed.

The initialized model does not perform any meaningful work, but as the learning progresses, the model outputs results similar to the actual value rather than a random value.

This is because the artificial neural network does not know anything before data is input, and the reason for initializing coefficients to random values is also the same. And as the data is read, the coefficients are updated little by little in the correct direction.

Through this update process, the artificial neural network can group similar acoustic data by classifying the input acoustic data. Then, the clustered sound data is registered in the database.

Then, the sound classification module 30 classifies the sound data classified according to the degree of similarity by the central processing unit 10 into general noise or accident noise.

That is, the sound classification module 30 compares the classified sound data with the sound data registered in the database to determine which type of sound is similar. Of course, it is also possible to compare and analyze by the deep learning method.

And, according to the degree of similarity, it is divided into general noise and accident noise.

For example, if the sound data in which the vehicle collides is similar, the sound classification module 30 determines that the collected sound is the sound caused by the vehicle collision.

In this way, if the sound is classified by accident type and it is determined as an emergency such as violence or a car crash, it can be linked with the IOT device to notify the relevant organization.

The IOT link unit 11 includes a search module 32 for searching for an institution related to the sound determined by the sound classification module 30; and a notification module 34 to notify the occurrence of an accident by transmitting a signal to the searched institution.

In more detail, the search module 32 fetches organizations related to vehicle accidents from the database. For example, hospitals, police stations, insurance companies, etc. will be withdrawn from the list.

Then, the notification module 34 notifies the withdrawn institutions of the occurrence of a vehicle accident through mail, message, or the like. At this time, since the GPS signal for the location where the IOT device is located is also transmitted, the relevant organizations can accurately determine the location of the accident in real time.

On the other hand, the sound data is analyzed by the noise control unit 13 to be divided into stages according to the sound intensity.

For example, the noise control unit 13 may divide the sound data into a plurality of stages by passing the band filter by interworking with the band filter for evaluating the intensity of the noise.

For example, it is divided into steps 1 to 10, the case with the lowest noise is set to step 1, and the case where the noise is the highest after gradually increasing is set to step 10.

In this way, the sound data is divided into stages according to the noise intensity.

And, according to the noise intensity divided in this way, the output unit 15 performs the announcement.

That is, when the noise level with respect to the surrounding sound is low, the noise control unit 13 lowers the output of the output unit 15 in conjunction to perform the announcement, and on the contrary, when the noise level is high, the output is increased and the announcement is performed.

As described above, by adjusting the intensity of the announcement according to the level of ambient noise, broadcasting can be effectively performed. At this time, the output unit 15 includes a speaker 19 and the like.

On the other hand, as another embodiment of the present invention, by additionally disposing the cough sound recognition unit 40, it is possible to detect a cough sound and determine whether a cold or a virus flu is present in real time.

As shown in FIG. 6 , the cough sound recognition unit 40 recognizes a cough sound among the sounds collected through the input unit and guides it to a related institution through the IOT link unit 11 .

In addition, there are various methods for recognizing a cough sound, and for example, a cough sound may be recognized by a method using STT (Sph To Text).

That is, the cough sound recognition unit 40 includes a vector extraction module 42 for extracting sound data corresponding to cough by analyzing a pattern from the sound data collected through the input unit; a database 48 in which cough sound data learned by machine learning or deep learning is stored; a determination module 44 for comparing the sound data extracted by the vector extraction module 42 with the cough sound data stored in the database 48 to determine whether or not there is a cough; When it is determined as coughing, it includes an output module 46 that notifies the relevant institution through the notification module 34 by transmitting a signal to the IOT interworking unit 11 .

In the cough sound recognition unit 40 , first, various cough sounds are collected, learned by machine learning or deep learning, and stored in the database 48 .

Then, the vector extraction module 42 digitizes the height (H), the interval (R), etc. of the waveform frequency by analyzing the sound data collected through the microphone from the outside, and converts it into a vector value. The vector extraction module 42 is connected to the time input module 49 to vectorize cough-related sound data only within a specific period.

For example, by allowing the vector extraction module 42 to vectorize the acoustic data only during a period during which a disease is transmitted, such as during an influenza epidemic, a cough sound can be recognized more effectively.

Then, the determination module 44 compares the sound data converted into a vector value with the cough data stored in the database 48 .

As a result of comparison, if similar cough data is detected, it is recognized as a cough sound.

And, in the case of a cough sound, the output module 46 transmits a signal to the IOT linkage unit 11 to notify the relevant institutions such as hospitals, public health centers, city halls, and quarantine institutions through the notification module 34 .

Accordingly, the institution can determine the location of the stop where the coughing sound is generated by the GPS signal and take measures against the spread of the flu virus.

In addition, by broadcasting a guide through the speaker of the commutator, the coughing pedestrian may be instructed to visit a related institution, such as a hospital.

The smart volume control system is composed of various hardware such as a microprocessor and software capable of executing the same, and is implemented in the form of program instructions that can be executed through these computer components and recorded on a computer-readable recording medium. The computer-readable recording medium may include program instructions, data files, data structures, etc. alone or in combination.

Claims (7)

an input unit 3 for collecting ambient sound;
an extraction unit 5 that analyzes the sound input in real time through the input unit 3 and simultaneously extracts sound and noise data;
a voice analyzer 7 for dividing and analyzing the extracted sound data by time;
a learning unit 9 for learning the acoustic data extracted through the extraction unit 5 through deep learning, extracting acoustic patterns, vectorizing them, analyzing the similarity, and classifying whether the analyzed acoustic patterns are general noise or accident noise;
an IOT interworking unit 11 that interworks with the IOT device and informs an external organization according to the result learned by the learning unit 9;
a noise control unit 13 for classifying the sound analyzed by the voice analysis unit 7 according to intensity; and
A smart volume control system (1) including an output unit (17) for performing a guide broadcasting according to the analysis results of the sound analysis unit (7) and the noise control unit (13). The method of claim 1,
The extraction unit 5 includes an input terminal 20 to which the sound wave signal transmitted from the input unit 3 is input; an amplifier 22 for amplifying the digital signal output from the input terminal 20; a band filter 24 for passing only frequencies of a specific band among the amplified signals; a detector 26 for detecting the filtered specific frequency; A smart volume control system (1) including a shaper (28) for shaping the signal output from the detector (26), and extracting a specific band frequency by the following equation.
The boundary frequency of the first capacitor: f1=1/2*π*R*C1 -------- Equation 1
Second capacitor boundary frequency: f2=1/2*π*R*C2 -------- Equation 2
(f1: first boundary frequency, f2: second boundary frequency, R: resistance, C1, C2: first and second capacitors) The method of claim 1,
The learning unit 9 includes: a central processing unit 10 for classifying the sound according to the degree of similarity by classifying the sound data by a deep learning method; A smart volume control system (1) including a sound classification module (30) for classifying sound data classified by the central processing unit (10) whether it is general noise or accidental noise. The method of claim 1,
The noise control unit 13 includes a band filter that evaluates the intensity of the noise step by step,
A smart volume control system (1) that distinguishes the intensity of noise by a certain range in the order of low noise level to high level level. 4. The method of claim 3,
The IOT linkage unit 11 includes a search module 32 for searching for an institution related to the sound determined by the sound classification module 30; A smart volume control system (1) including a notification module (34) notifying the occurrence of an accident by transmitting a signal to the searched institution. 6. The method of claim 5,
The search module 32 is a smart volume control system (1) that searches for and selects an organ related to the corresponding sound from the list of external organs stored in the database (48). 6. The method of claim 5,
It further includes a cough sound recognition unit 40, and the cough sound recognition unit 40 analyzes a pattern from the acoustic data collected through the input unit and extracts acoustic data corresponding to the coughing vector extraction module 42; a database 48 in which cough sound data learned by machine learning or deep learning is stored; a determination module 44 for comparing the sound data extracted by the vector extraction module 42 with the cough sound data stored in the database 48 to determine whether or not there is a cough; When it is determined as coughing, it includes an output module 46 that notifies the relevant institution through the notification module 34 by transmitting a signal to the IOT linkage unit 11,
A smart volume control system that analyzes sound patterns and performs vector extraction only during a specific period by interlocking the vector extraction module 42 with the time input module 49.

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