# Voice Detection and Deep Learning Algorithms Application in Remote English Translation Classroom Monitoring

Authors: Zhenyu Niu
Journal: Mobile Information Systems
 (2022)

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## Abstract

With the continuous development of cellular networks, the traffic from voice services increases gradually. The wireless sensor network (WSN) is a distributed network consisting of a large number of peripheral nodes distributed in the surveillance area. The nodes in the network complete it in a self-organizing form, and the sink node collects the data from each sensor node. When sending data, the nodes near the receiver will quickly run out of energy and cannot perform further transmission tasks. The resulting “power supply emptiness” problem has a great impact on network performance. Therefore, the power consumption of the network must be considered when designing the WSN routing algorithm. In order to effectively improve students’ academic performance and monitor students’ teaching conditions, the classroom remote monitoring system places two cameras in the university’s English translation classroom and uses technology to merge the information to execute the entire process. By recording the course, we can save the teacher’s classroom content and the student’s classroom performance and upload the recorded video in real time. In addition, the classroom remote monitoring system is a multiclient system, divided into teacher and student terminals. The user can log in, watch the video, and perform other necessary operations.


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## Body

## 1. Introduction

The voice quality detection algorithm used in this article is a combination of the PESQ algorithm and the sine detection algorithm, which can help identify voice quality problems in different types of voice networks in more detail. By studying PESQ and sine analysis algorithms, a feasible mobile communication system optimization scheme is proposed. Study the algorithm principles and test plans for evaluating voice quality [1]. By analyzing the recommendations of the ITU-T algorithm for evaluating voice quality, it is possible to understand the scope and accuracy of common objective evaluation algorithms and choose an appropriate evaluation method [2]. Due to the heavy burden of subjective assessment, it is not suitable for daily work. Therefore, the voice quality assessment system uses the PESQ method to simulate the hearing process of the human ear to measure the perceptible voice quality and transmit the corresponding MOS value [3]. Wireless sensor network technology has been widely used in military, medical, and environmental monitoring fields, and is one of the most important technologies today. The wireless sensor network consists of a large number of small sensor nodes, which are randomly distributed in certain areas for data collection [4]. They have specific energy, storage capacity, communication capacity, and computing power. However, resources are limited, and traditional network protocols cannot be directly applied to wireless sensor networks. Therefore, it is very meaningful and valuable to study energy-efficient and efficient wireless sensor network routing protocols, and it is also one of the current research hotspots [5]. This article mainly starts from the energy-saving aspect and studies the routing protocol of wireless sensor networks. First, for the problems of poor convergence and uneven energy consumption of the HEED routing protocol, an OPFH routing protocol based on the OPTICS clustering algorithm is proposed [6]. The protocol first uses the OPTICS clustering algorithm to divide the network into multiple first-level clusters, and then simultaneously conducts cluster head elections in each first-level cluster; in the process of candidate cluster heads competing to produce the final cluster head, the current energy and the distance to the base station are used as input parameters, and the competing cluster radius is used as the output parameter. Fuzzy logic control is used to obtain the optimal cluster radius. According to the obtained optimal cluster radius, the final cluster head is generated by competition, and finally, the cluster head nodes are established. Multihop routing mechanism to send data to the base station. Simulation experiments show that the energy consumption between clusters and within clusters is more balanced [7]. This effectively extends the life of the network. The remote monitoring system of English translation classroom usually consists of two parts. The top layer is the link layer, which is used to support application layer data requirements and related control of link-layer devices [8]. The main functions are video recording, video transmission, and camera control. The video collected on the link layer is sent to the host via Ethernet and the host can receive instructions on how to run the template matching algorithm. In addition, the host can control the camera through the processor. The bottom layer is the application layer that uses C# for programming and rents Alibaba Cloud servers to store data and videos. At the same time, we use CDN’s complete site acceleration function to enable users to understand and watch videos in time and improve the round-trip speed of video data packets [9].


## 2. Related Work

The literature introduces the characteristics of WSN routing algorithms for mobile convergence and classifies and describes mobile strategies and data acquisition methods [10]. It describes some typical routing protocols based on mobile synchronization, distinguishes them from several aspects such as location detection, path planning, and data collection methods, and compares the performance of different typical routing protocols [11]. The literature introduces the problem of unreasonable cluster head selection and high-energy consumption of the LEACH algorithm in long-distance transmission and proposes an improvement plan [12]. In the improved algorithm, the role of the node is determined by two screenings, thus providing the preferred cluster head for high-energy nodes, while controlling the number and distribution of cluster heads [13]. Sink nodes calculate the shortest transmission path between clusters, compare the energy consumption of communication, and create routes between clusters based on the results. The literature describes the nature of the mobile WSN routing algorithm used for convergence. We propose an energy balance routing algorithm based on mobile receivers [14]. The algorithm starts with cluster split mode, data collection mode, and routing [15]. The cluster head is given by the receiver node, and the cluster is split according to the K-means algorithm. At the same time, according to different data delay requirements, combined with different data collection methods, the routing of the sink node is planned. The literature introduces various factors that affect voice quality in network operations and provides a corresponding test plan for each element of various voice quality defects [16]. The literature introduces the sine analysis algorithm, which can analyze the discrete sine sequence received by the receiver to determine whether it contains the transmitted sine wave, silent or intermittent [17]. By combining sine recognition algorithms and algorithms, it is possible to identify voice quality problems in different types of voice networks in more detail.


## 3. Voice Quality Detection and Wireless Sensor Network Model

### 3.1. Voice Quality Detection

Because the speech has the characteristics of short-term stability, it is divided into multiple small segments. This process is called framing. After framing, the endpoint detection problem is transformed into a frame-level speech/nonspeech (0/1) decision. The whole system is divided into training and testing phases, as shown in Figure1.Figure 1
Flow chart of the deep learning voice endpoint detection system.In the training phase, training data and corresponding training targets need to be generated. Assuming that the interference noise is additive noise, the mixed speech can be directly obtained by adding the pure speech and the noise. We have the following:(1)xt=st+nt.Average loss:(2)ℒΘ=1T∑t=1TLfXt,f;Θ,yt.Fitting of the model to the data:(3)Θ^=argminθ1T∑t=1TLfXt,f;Θ,yt.For the binary classification problem of speech endpoint detection, cross-entropy is usually used as the loss function:(4)L=ytlogyt^+1−ytlog1−yt^.At present, the expressive ability of deep learning models has been continuously enhanced, gradually replacing the role of feature design and combination in the modeling process. The logarithmic amplitude spectrum is one of the simplest, most straightforward, and most commonly used features. Because the amplitude spectrum only transforms the speech in the time domain to the frequency domain, the method of using the amplitude spectrum as input can also be called an end-to-end method. The formula for calculating the amplitude spectrum is(5)Xt,f=Xt,f2real+Xt,f2imag.The voice endpoint detection method based on deep learning regards VAD as a binary classification problem, and its calculation formula is(6)ACC=TT+F.In practical applications, the ratio of the voice part to the nonvoice part is usually not 1:1. In order to better evaluate the performance of the model, usually refer to the voice hit rate (HIT) and false alarm rate (FA), shown as follows:(7)HIT=TPTP+FN,(8)FA=FPFP+TN.Calculation formula for AUC is as follows:(9)AUC=∑i=1sranki−MM+1/2M∗N.All experiments in this article are performed on the TIMIT data set. TIMIT is a manually labeled data set, and it is easy to obtain training targets from the labeled word transcription files. In the label file of TIMIT, each sentence of speech has a corresponding word transcription file, and the file contains the time boundary of all words. Table1 shows an example of a word transcription file, where the start time and the end time both represent the sampling points at the 16 kHz sampling rate. For example, the word “she” starts at the 9640th sample point and ends at the 12783rd sample point. We convert the time boundary into a label corresponding to the frame to get the training target.Table 1
Word transcription file.
Start timeStop timeWord964012783She1278317103Had1710318760Your1876024104Dark2410429179Suit2917931880In3188038568Greasy3856845119Wash4562451033Water5237855461AllRandomly select 2000 pure speech generation training sets from the TIMIT training set, and use the TIMIT core test set to generate the test set. The TIMIT core test set was recorded by 24 people, each with 8 sentences and a total of 192 sentences. These voices usually contain fewer silent segments. In order to balance the proportion of speech and nonspeech, in all experiments in this article, the selected speech is connected with a random length of the silent segment, so that the proportion of speech frames accounts for about 60%.There are five types of noise for generating training speech: speech shape noise (SSN), noisy human voice (babble), factory noise (factory 1), destroyer power room noise (destroy engine), and destroyer operations room noise (destroyer operations room), the latter four noises are all from the NOISEX-92 data set. This combination has been proven to have a certain complementarity and can cover more noise scenes. In order to examine the performance of the model in various environments, in addition to the types of noise used in training, we also tested two types of noise that belong to the NOISEX-92 data set: another type of factory noise (factory 2) and pirate noise (buccaneer) and Two types of noise from the CHiME-4 data set: bus noise (bus) and street noise (street).In order to ensure the independence of training noise and test noise, each noise is divided into two nonoverlapping parts to generate a training set and a test set. In order to maintain the diversity of samples, noise fragments are randomly cut from long noise before mixing. The signal-to-noise ratios of the generated training set are −5 dB and 0 dB, and the signal-to-noise ratios of the generated test set are −5 dB, 0 dB, and 5 dB. A total of 2000 (pure speech) × 5 (noise types) × 2 (signal-to-noise ratio) = 20000 pieces in the training set, and 10% in the verification set. In the end, the speech of the training set is about 30 hours. The trained and untrained noise is used to generate the test set. There are five types of training noise. The corresponding test voices are 192 × 5 × 3 = 2880 pieces; there are four kinds of untrained noises, and the corresponding test voices are 192 × 4 × 3 = 2304 pieces. Since the sampling rate of the TIMIT data set is 16 kHz, all signals are resampled to 16 kHz before mixing, as shown in Table2.Table 2
Detailed model parameters.
ModelFloorEnterHyperparameterOutputSohnVAD—T × F—T × lCNNConvolutional layer 140 × 5Number of units 32; convolution kernel 338 × 32Convolutional layer 238∗32Number of units 64; convolution kernel 336 × 64Fully connected layer 12304Number of units 6464Fully connected layer 264Number of units 11LSTMLong and short-term memory layer 1T × 200Number of units 128T × l.28Long and short-term memory layer 2T × l28Number of units 64T × 64Long and short-term memory layer 3T × 64Number of units 1T × lCLDNNConvolutional layer 1T × 40 × 5Number of units 32; convolution kernel 3T × 38 × 32Convolutional layer 2T × 38 × 32Number of units 64; convolution kernel 3T × 36 × 64Long- and short-term memory layerT × 2304Number of units 128;T × 64Fully connected layerT × 64Number of units 1T × lProposed methodSame as CLDNNSame as CLDNNSame as CLDNNSame as CLDNNWe compare the two-stage training CLDNN with four baseline systems, including one of the most commonly used statistical methods and three methods based on deep learning. The parameters of the model are shown in Table2. Among them, SohnVAD represents the statistical method proposed by Sohn et al. In the table, T represents the number of frames, F represents the number of frequency bands, and T is 100 in the experiment. The input of all models is the characteristics of the current frame and the two frames before and after. This form of frame expansion provides the model with the most relevant context information for the current frame.Since the two-stage training method can be regarded as an augmentation of the data, the better expression of the underlying convolution is due to the richer data patterns received, so we have verified all the methods on two scales of data. First, we conducted experiments on about 3 hours of training data. Table3 lists the experimental results. The data shown in the table are the best results that the model can obtain under the same conditions using the same test set.Table 3
Comparison of AUC indicators of voice endpoint detection models based on deep learning.
Signal-to-noise ratioTypes of trained noiseUntrained noise types−5 dB0 dB5 dBAverage−5 dB0 dB5 dBAverageSohnVAD56.1362.8368.5162.4965.1470.9776.7770.96CNN75.7182.8487.0481.8671.1277.6283.7177.48LSTM78.9284.3287.3483.5276.8984.9787.9083.25CLDNN87.4391.1392.8090.4579.6987.9192.7186.77Proposed method89.8894.1795.6793.2480.3088.7895.1088.06It can be seen from Table3 that the statistical method SohnVAD performs worse than the deep learning method. LSTM shows better performance than CNN in all noise scenarios and a relative average increase of 2.02% and 7.44% in trained and untrained noise scenarios. Combining the advantages of CNN and LSTM, CLDNN is 10.49% higher than CNN in trained noise scenes, 8.29% higher than LSTM, and 11.99% and 4.22% higher in untrained noise scenes. Compared with the CLDNN baseline, the training method proposed in this article has a relative improvement of 3.08% in the trained noise scene and 1.48% in the untrained noise scene.Figure2 shows the ROC curve of each model tested in the trained noise scene and the untrained noise scene when the signal-to-noise ratio is 0 dB. The lower the false alarm rate (the smaller the abscissa), the better and the higher the hit rate (the larger the ordinate). Therefore, the more the ROC overall curve is to the upper left corner, the better the overall hit rate and false alarm rate of the system. The solid line in the curve represents the method proposed in this article, which has the best performance among the four methods.Figure 2
Comparison of ROC curves of various models.Figure3 shows the comparison between training data of different sizes. Compared with the model trained on 3 hours of data, when the training data is increased by 10 times, the improvement of CLDNN by the two-stage training method is reduced. It is foreseeable that when the amount of training data continues to increase, the gap between the two methods will be further reduced. The experiment proves that the two-stage training method has more advantages in small sample tasks.Figure 3
Comparison of model performance on training data of different scales.


### 3.2. Wireless Sensor Network

The trilateral positioning method is the most basic source node positioning algorithm. The basic idea is that when the distance from an unknown node to at least three anchor nodes is known, the geometric characteristics of the intersection of three circles at one point can be used to calculate its coordinates.Then, there are the following equations:(10)x−xa2+x−xa2=la,x−xb2+x−xb2=lb,x−xc2+x−xc2=lc.Calculating formula (10) shows that the coordinates of the unknown node O are(11)xy=2xa−xc2ya−yc2xb−xc2yb−yc−1xa2−xc2+ya2−yc2+dc2−da2xb2−xc2+yb2−yc2+dc2−db2.These variables should satisfy the following formula:(12)x−x12+y−y12=l12⋮x−xn2+y−yn2=ln2.Starting from the second line, each line is subtracted from the previous line to obtain(13)x12−xn2+2x1−xnx+y12−yn2−2y1−yny=l12−ln2,xn−12−xn2+2xn−1−xnx+yn−12−yn2−2yn−1−yny=ln−12−ln2.Remember(14)B=x12−xn2+y12−yn2+ln2−l12⋮xn−12−xn2+yn−12−yn2+ln2−ln−12.Then, the equationAX = B can be obtained, so(15)X=ATA−1ATB.Then, the coordinates of nodeO can be estimated by the following formula:(16)xm,ym=∑m=1MxmM,∑m=1MxyM.The original LRMD model can be expressed as(17)minU,VM−UVTℓp.The original LRMD can be transformed into a matrix reconstruction model by adding an orthogonal projection operator, which can be expressed as(18)minU,VPΩM−UVTℓp.We have the following:(19)PΩMij=Mij,i,j∈Ω0,otherwise.It can be described as(20)minU,VPΩM−UVTMOG.The matrixX can be defined as(21)X2,1=∑i=1m∑j=1nXij21/2.(22)Xp=∑i=1m∑j=1nXijp1/p.(23)XF=∑i=1m∑j=1nXij21/2.The nearest neighbor operator can be defined as(24)proxτFXM=argminXτFX+μ2X−MF2.The structural threshold operator is defined as(25)proxτX2,1M=Jτ/μM.


## 3.1. Voice Quality Detection

Because the speech has the characteristics of short-term stability, it is divided into multiple small segments. This process is called framing. After framing, the endpoint detection problem is transformed into a frame-level speech/nonspeech (0/1) decision. The whole system is divided into training and testing phases, as shown in Figure1.Figure 1
Flow chart of the deep learning voice endpoint detection system.In the training phase, training data and corresponding training targets need to be generated. Assuming that the interference noise is additive noise, the mixed speech can be directly obtained by adding the pure speech and the noise. We have the following:(1)xt=st+nt.Average loss:(2)ℒΘ=1T∑t=1TLfXt,f;Θ,yt.Fitting of the model to the data:(3)Θ^=argminθ1T∑t=1TLfXt,f;Θ,yt.For the binary classification problem of speech endpoint detection, cross-entropy is usually used as the loss function:(4)L=ytlogyt^+1−ytlog1−yt^.At present, the expressive ability of deep learning models has been continuously enhanced, gradually replacing the role of feature design and combination in the modeling process. The logarithmic amplitude spectrum is one of the simplest, most straightforward, and most commonly used features. Because the amplitude spectrum only transforms the speech in the time domain to the frequency domain, the method of using the amplitude spectrum as input can also be called an end-to-end method. The formula for calculating the amplitude spectrum is(5)Xt,f=Xt,f2real+Xt,f2imag.The voice endpoint detection method based on deep learning regards VAD as a binary classification problem, and its calculation formula is(6)ACC=TT+F.In practical applications, the ratio of the voice part to the nonvoice part is usually not 1:1. In order to better evaluate the performance of the model, usually refer to the voice hit rate (HIT) and false alarm rate (FA), shown as follows:(7)HIT=TPTP+FN,(8)FA=FPFP+TN.Calculation formula for AUC is as follows:(9)AUC=∑i=1sranki−MM+1/2M∗N.All experiments in this article are performed on the TIMIT data set. TIMIT is a manually labeled data set, and it is easy to obtain training targets from the labeled word transcription files. In the label file of TIMIT, each sentence of speech has a corresponding word transcription file, and the file contains the time boundary of all words. Table1 shows an example of a word transcription file, where the start time and the end time both represent the sampling points at the 16 kHz sampling rate. For example, the word “she” starts at the 9640th sample point and ends at the 12783rd sample point. We convert the time boundary into a label corresponding to the frame to get the training target.Table 1
Word transcription file.
Start timeStop timeWord964012783She1278317103Had1710318760Your1876024104Dark2410429179Suit2917931880In3188038568Greasy3856845119Wash4562451033Water5237855461AllRandomly select 2000 pure speech generation training sets from the TIMIT training set, and use the TIMIT core test set to generate the test set. The TIMIT core test set was recorded by 24 people, each with 8 sentences and a total of 192 sentences. These voices usually contain fewer silent segments. In order to balance the proportion of speech and nonspeech, in all experiments in this article, the selected speech is connected with a random length of the silent segment, so that the proportion of speech frames accounts for about 60%.There are five types of noise for generating training speech: speech shape noise (SSN), noisy human voice (babble), factory noise (factory 1), destroyer power room noise (destroy engine), and destroyer operations room noise (destroyer operations room), the latter four noises are all from the NOISEX-92 data set. This combination has been proven to have a certain complementarity and can cover more noise scenes. In order to examine the performance of the model in various environments, in addition to the types of noise used in training, we also tested two types of noise that belong to the NOISEX-92 data set: another type of factory noise (factory 2) and pirate noise (buccaneer) and Two types of noise from the CHiME-4 data set: bus noise (bus) and street noise (street).In order to ensure the independence of training noise and test noise, each noise is divided into two nonoverlapping parts to generate a training set and a test set. In order to maintain the diversity of samples, noise fragments are randomly cut from long noise before mixing. The signal-to-noise ratios of the generated training set are −5 dB and 0 dB, and the signal-to-noise ratios of the generated test set are −5 dB, 0 dB, and 5 dB. A total of 2000 (pure speech) × 5 (noise types) × 2 (signal-to-noise ratio) = 20000 pieces in the training set, and 10% in the verification set. In the end, the speech of the training set is about 30 hours. The trained and untrained noise is used to generate the test set. There are five types of training noise. The corresponding test voices are 192 × 5 × 3 = 2880 pieces; there are four kinds of untrained noises, and the corresponding test voices are 192 × 4 × 3 = 2304 pieces. Since the sampling rate of the TIMIT data set is 16 kHz, all signals are resampled to 16 kHz before mixing, as shown in Table2.Table 2
Detailed model parameters.
ModelFloorEnterHyperparameterOutputSohnVAD—T × F—T × lCNNConvolutional layer 140 × 5Number of units 32; convolution kernel 338 × 32Convolutional layer 238∗32Number of units 64; convolution kernel 336 × 64Fully connected layer 12304Number of units 6464Fully connected layer 264Number of units 11LSTMLong and short-term memory layer 1T × 200Number of units 128T × l.28Long and short-term memory layer 2T × l28Number of units 64T × 64Long and short-term memory layer 3T × 64Number of units 1T × lCLDNNConvolutional layer 1T × 40 × 5Number of units 32; convolution kernel 3T × 38 × 32Convolutional layer 2T × 38 × 32Number of units 64; convolution kernel 3T × 36 × 64Long- and short-term memory layerT × 2304Number of units 128;T × 64Fully connected layerT × 64Number of units 1T × lProposed methodSame as CLDNNSame as CLDNNSame as CLDNNSame as CLDNNWe compare the two-stage training CLDNN with four baseline systems, including one of the most commonly used statistical methods and three methods based on deep learning. The parameters of the model are shown in Table2. Among them, SohnVAD represents the statistical method proposed by Sohn et al. In the table, T represents the number of frames, F represents the number of frequency bands, and T is 100 in the experiment. The input of all models is the characteristics of the current frame and the two frames before and after. This form of frame expansion provides the model with the most relevant context information for the current frame.Since the two-stage training method can be regarded as an augmentation of the data, the better expression of the underlying convolution is due to the richer data patterns received, so we have verified all the methods on two scales of data. First, we conducted experiments on about 3 hours of training data. Table3 lists the experimental results. The data shown in the table are the best results that the model can obtain under the same conditions using the same test set.Table 3
Comparison of AUC indicators of voice endpoint detection models based on deep learning.
Signal-to-noise ratioTypes of trained noiseUntrained noise types−5 dB0 dB5 dBAverage−5 dB0 dB5 dBAverageSohnVAD56.1362.8368.5162.4965.1470.9776.7770.96CNN75.7182.8487.0481.8671.1277.6283.7177.48LSTM78.9284.3287.3483.5276.8984.9787.9083.25CLDNN87.4391.1392.8090.4579.6987.9192.7186.77Proposed method89.8894.1795.6793.2480.3088.7895.1088.06It can be seen from Table3 that the statistical method SohnVAD performs worse than the deep learning method. LSTM shows better performance than CNN in all noise scenarios and a relative average increase of 2.02% and 7.44% in trained and untrained noise scenarios. Combining the advantages of CNN and LSTM, CLDNN is 10.49% higher than CNN in trained noise scenes, 8.29% higher than LSTM, and 11.99% and 4.22% higher in untrained noise scenes. Compared with the CLDNN baseline, the training method proposed in this article has a relative improvement of 3.08% in the trained noise scene and 1.48% in the untrained noise scene.Figure2 shows the ROC curve of each model tested in the trained noise scene and the untrained noise scene when the signal-to-noise ratio is 0 dB. The lower the false alarm rate (the smaller the abscissa), the better and the higher the hit rate (the larger the ordinate). Therefore, the more the ROC overall curve is to the upper left corner, the better the overall hit rate and false alarm rate of the system. The solid line in the curve represents the method proposed in this article, which has the best performance among the four methods.Figure 2
Comparison of ROC curves of various models.Figure3 shows the comparison between training data of different sizes. Compared with the model trained on 3 hours of data, when the training data is increased by 10 times, the improvement of CLDNN by the two-stage training method is reduced. It is foreseeable that when the amount of training data continues to increase, the gap between the two methods will be further reduced. The experiment proves that the two-stage training method has more advantages in small sample tasks.Figure 3
Comparison of model performance on training data of different scales.


## 3.2. Wireless Sensor Network

The trilateral positioning method is the most basic source node positioning algorithm. The basic idea is that when the distance from an unknown node to at least three anchor nodes is known, the geometric characteristics of the intersection of three circles at one point can be used to calculate its coordinates.Then, there are the following equations:(10)x−xa2+x−xa2=la,x−xb2+x−xb2=lb,x−xc2+x−xc2=lc.Calculating formula (10) shows that the coordinates of the unknown node O are(11)xy=2xa−xc2ya−yc2xb−xc2yb−yc−1xa2−xc2+ya2−yc2+dc2−da2xb2−xc2+yb2−yc2+dc2−db2.These variables should satisfy the following formula:(12)x−x12+y−y12=l12⋮x−xn2+y−yn2=ln2.Starting from the second line, each line is subtracted from the previous line to obtain(13)x12−xn2+2x1−xnx+y12−yn2−2y1−yny=l12−ln2,xn−12−xn2+2xn−1−xnx+yn−12−yn2−2yn−1−yny=ln−12−ln2.Remember(14)B=x12−xn2+y12−yn2+ln2−l12⋮xn−12−xn2+yn−12−yn2+ln2−ln−12.Then, the equationAX = B can be obtained, so(15)X=ATA−1ATB.Then, the coordinates of nodeO can be estimated by the following formula:(16)xm,ym=∑m=1MxmM,∑m=1MxyM.The original LRMD model can be expressed as(17)minU,VM−UVTℓp.The original LRMD can be transformed into a matrix reconstruction model by adding an orthogonal projection operator, which can be expressed as(18)minU,VPΩM−UVTℓp.We have the following:(19)PΩMij=Mij,i,j∈Ω0,otherwise.It can be described as(20)minU,VPΩM−UVTMOG.The matrixX can be defined as(21)X2,1=∑i=1m∑j=1nXij21/2.(22)Xp=∑i=1m∑j=1nXijp1/p.(23)XF=∑i=1m∑j=1nXij21/2.The nearest neighbor operator can be defined as(24)proxτFXM=argminXτFX+μ2X−MF2.The structural threshold operator is defined as(25)proxτX2,1M=Jτ/μM.


## 4. Design and Application of the Remote English Translation Classroom Monitoring System

### 4.1. Demand Analysis of the Remote English Translation Classroom Monitoring System

Traditional classroom video surveillance only collects, transmits, and stores video data, and the video information can only be obtained through later viewing by relevant personnel. However, because the amount of video data is huge and contains a lot of irrelevant information, it takes a lot of manpower. In addition, manual viewing of video information is prone to misjudgment, so the classroom monitoring system needs to be further intelligent. Therefore, a video surveillance system that relies on artificial intelligence technology to identify abnormal behaviors in the classroom in real-time should be designed. In this article, the student’s abnormal behavior recognition system is mainly divided into three parts, which are the embedded video data acquisition module, server intelligent processing module, and mobile phone APP alarm module. The server intelligent processing module mainly includes two parts: student and mobile phone target detection, and students’ abnormal behavior recognition. This chapter puts forward the functional requirements of the system by analyzing the characteristics of the three abnormal behaviors of students in the classroom and then gives the overall design of the system.Compared with an open environment or an indoor environment with a large flow of people, the video surveillance system in the classroom environment has unique environmental characteristics, that is, the flow of people is small, so the surveillance video can be obtained by video capture at a fixed position and a fixed angle. No need to consider tracking requirements. Through the analysis of the classroom environment, it is found that the first classroom is divided into regular classrooms and lecture rooms. The camera can be placed in the two corners of the classroom or in the middle of the classroom. After actual experiments, the latter was chosen for this design (taking into account the distortion of the side camera image), but under any of the abovementioned conditions, the camera position will not change. It should be noted that the camera’s angle of view is best to look down at the students so that the camera can more clearly capture the movement information of each student. But despite this, some students will still be occluded, and when the length of the classroom exceeds 5 m, it has a smaller resolution for the student targets relatively close to the back of the classroom. In order to compensate the influence of classroom scene characteristics on behavior recognition, a higher resolution camera should be used in the system design. The resolution of the Logitech C270 camera can reach 1920 × 1080, which can compensate for the problems of occlusion and low resolution of the students behind. In the classroom scene, the fixed background target detection method is not suitable for this system, because the student’s range of motion is narrow, and the placement of the object may change. Therefore, the images suitable for this design are selected from the COCO data set as the training data set.In the classroom scenario, when students have abnormal behaviors of playing with mobile phones, there will be obvious mobile phone objects appearing in the image, and the mobile phone object is the closest to a certain student object. The student object’s face is generally downward and part of the mobile phone object. The coincidence of the behavior and the duration of the behavior are generally more than 10 seconds. As for the behavior of sleeping in class, through image analysis, it is found that students’ sitting posture has changed significantly. The most fundamental feature is that they can hardly see any facial features, and this state will last for a long time. Student communication is an abnormal behavior of multiple people at the same time. Generally, there are two or more students participating, one after the other, or one on the left and the other on the right. When this behavior occurs, the student’s head will continue to rotate forward, backward, left, and right. The position of the student’s entire body has undergone a major change, which exceeds the area of activity during normal lectures. The fundamental purpose of this system is to realize the recognition of abnormal behaviors of students in the classroom, but the recognition of abnormal behaviors is based on target detection. In order to achieve a more accurate recognition of abnormal behaviors, both the recall and accuracy of student target detection should reach more than 90%, and the recall and precision of small target mobile phones should both reach more than 85%.The intelligent monitoring system designed in this article is mainly aimed at indoor collective activities such as meeting rooms, classrooms, and other places, and is mainly used to monitor the behavior of members under the situation of teachers teaching or leaders meeting. Because this type of scene is relatively small, the cost of its development must be low, so that the video surveillance system can be widely used. Based on the abovementioned reasons, this topic adopts a cost-effective embedded method for development. In addition, the use of wired network transmission makes networking easier in this type of scenario. The system should also have the performance of intelligent analysis, which can intelligently detect the relevant characters in the collected video. According to the characteristics and requirements of the classroom intelligent monitoring system, the system needs to meet the following requirements:(1)
The embedded camera must be able to realize a real-time collection of video data, video encoding, and transmission so that the terminal device can display video images without jams. Therefore, the embedded camera is required to collect video data at a higher sampling frequency.(2)
The embedded camera adopts digital, which is convenient for information transmission, storage, and processing, easy to connect with other communication equipment, and has strong compression potential, which can provide clearer video images. Due to its strong anti-interference ability, the image is less distorted.(3)
Data transmission is carried out through the wired network. Since the video data collected by the built-in camera must be transmitted through the network, the camera is connected to the router through a network cable in order to transmit the video data.(4)
With image analysis and processing capabilities, the collected video data is processed by behavior recognition algorithms, such as playing with mobile phones, sleeping, and communicating with each other. The behavior recognition algorithm detects the abovementioned behaviors and immediately transmits them to the teacher’s mobile APP terminal, and the APP receives messages and realizes the alarm in the form of sound effects, vibration, and a pop-up message box.(5)
The server-side and mobile APPs have permission restrictions. When the teacher watches the monitoring screen received by the server and APP, he needs to enter the password when registering the server and APP. Only when the password is consistent with the registration password can he access the server and APP. In addition, when the administrator logs in to the server to process the received video data, it also needs to be consistent with the password during registration.


### 4.2. System Overall Framework Design

In order to enable the system to have the scalability of the number of users and the flexibility of the layout of the hardware facilities, the system chose to access the local area network, transplant the camera application on the ARM9 development board, and send the collected data to the server, and the server performs intelligence on the video data. The mobile APP can receive the identified abnormal behavior video data after communicating with each other by accessing the server, as shown in Figure4.Figure 4
System frame diagram.According to the block diagram of the system, the system is mainly composed of an equipment terminal, router, internet, server intelligent detection module, and teacher’s mobile APP terminal. This system uses the ARM-LINUX platform to collect video images using the video data collection module, sends the collected data to the server discovery module, and transmits the identification results of abnormal behavior to the teacher’s mobile APP terminal.Embedded camera module: under the ARM-LINUX platform, the video data is obtained through the V4L2 driver framework. Use the x264 video encoding library to implement H.264 encoding. Router: the main function is to connect the embedded camera to the network. Server intelligent detection module: it mainly uses FFmpeg and H.264 to decode the video data transmitted from the device terminal, YOLOv3 intelligent detection, and stores the video data for administrators and teachers to view after logging in.Teachers’ mobile phone APP terminal: receive the detection results of the server, such as detecting abnormal behavior of students, transmitting the image data to the APP, generating sound effects, vibrations, and pop-up message boxes at the same time. After seeing the alarm information, the teacher will check the image data received by the APP so you can locate a specific student.


### 4.3. System Development Environment Design

The following are the specific steps to build the environment:(1)
Install the virtual machine on the PC and then install the Ubuntu 12.04 version of the LINUX operating system on the virtual machine(2)
After entering the LINUX system, copy the compressed package to the LINUX system(3)
Decompress the cross-compiler compressed package through commands on the terminal of the LINUX system(4)
Configure the environment variables of the cross-compiler and check whether the cross-compiler is installed successfullyAs we all know, for the LINUX system, there are very strict regulations and requirements on power consumption, functions, and costs. At the same time, it has a variety of different hardware interfaces to achieve strict management of the file system. In addition, it also has a series of different advantages, for example, easy to transplant, so the system has been generally welcomed by the industry.Bootloader is the first program executed after the embedded device is powered on, and can read and write flash, initialize SDRAM, initialize clock, initialize serial port, etc., to achieve the function of starting the kernel. This article uses U-Boot 1.1.6, the specific operations are as follows:(1)
Download the U-Boot 1.1.6 version from a website, transfer it to the LINUX system, enter the directory, and decompress it through commands(2)
Download the patch file that matches U-Boot 1.1.6, transfers it to the LINUX system, and open the U-Boot patch by command(3)
Configure and compile U-Boot is configured through commands, and then make compiles(4)
Use the programming tool to program the compiled u-boot.bin file into the ARM development board


### 4.4. Video Capture Module Design

In order to make full use of LINUX system resources, the camera application originally designed uses the V4L2 interface to realize the collection of image data. The collection of image data is mainly divided into three steps, the first is the initialization operation of the image data collection, the second is the application for the operating space memory, and the last is the collection operation of the image data.In the process of information transmission, the TCP/IP protocol is in a basic position, and it has a very common practical application in the internet field. For TCP/IP, it includes different levels, such as the hardware interface layer and transport layer. Through induction and summary, it can be concluded that TCP/IP has many advantages, and its basic structure is not complicated. This is because the protocol integrates the physical layer and data link layer of IOS to form a hardware interface layer and a session layer. And the presentation layer is included in the application layer.In the design, the communication protocol between the embedded development card and the server uses the connection-oriented TCP protocol. The TCP protocol is used because the TCP protocol has the following characteristics:(1)
TCP is a connection-oriented protocol. Before data exchange is realized between the development board and the application program of the server, the connection must be established through three handshakes, and the link is always occupied during the communication again. Until the data exchange is completed, the two parties will dismantle the link through four waves of hands.(2)
TCP has the characteristics of high reliability of data transmission. After the connection between the development board and the server is established, the development board will send new data to the server only after the server receives the correct data. If the development board does not receive the confirmation message from the server, the development board will retransmit the data until the server sends back the confirmation message or the message is sent over time.(3)
TCP is a full-duplex communication protocol. The development board and the server can send information to each other at the same time.(4)
TCP has the characteristics of sliding window control. TCP can determine the transmitted data traffic according to specific network conditions.


### 4.5. System Test

The peripheral equipment and embedded system of the core board, as well as the video data acquisition application program run on TQ2440. The specific configuration parameters of the core board are shown in Table4.Table 4
Embedded core board configuration parameters.
ConfigurationParameterCPU processorS3C2440, stablemaximum frequency 400 MHSDRAM64 MBNANDFLASH256 MBNORFLASH2 MBCore power1.25 VUSB cameraLogitech C270Embedded operating systemLINUX2.6.22.6 kernel versionBehavior detection server: considering real-time requirements, the classroom behavior detection and recognition part cannot be directly run on the mobile platform, so this article uses the upper computer-side server to implement the main algorithm part of the classroom behavior detection and recognition. The server uses i79700CPU and GTX1080GPU as the core computing unit and uses SSH to communicate with the mobile phone APP. The specific parameters are shown in Table5.Table 5
Behavior detection server configuration parameters.
ConfigurationParameterCPUi79700kGPUGTX1080System memory16G DDR4GPU memory8 GPower2 kWStorage500 G


## 4.1. Demand Analysis of the Remote English Translation Classroom Monitoring System

Traditional classroom video surveillance only collects, transmits, and stores video data, and the video information can only be obtained through later viewing by relevant personnel. However, because the amount of video data is huge and contains a lot of irrelevant information, it takes a lot of manpower. In addition, manual viewing of video information is prone to misjudgment, so the classroom monitoring system needs to be further intelligent. Therefore, a video surveillance system that relies on artificial intelligence technology to identify abnormal behaviors in the classroom in real-time should be designed. In this article, the student’s abnormal behavior recognition system is mainly divided into three parts, which are the embedded video data acquisition module, server intelligent processing module, and mobile phone APP alarm module. The server intelligent processing module mainly includes two parts: student and mobile phone target detection, and students’ abnormal behavior recognition. This chapter puts forward the functional requirements of the system by analyzing the characteristics of the three abnormal behaviors of students in the classroom and then gives the overall design of the system.Compared with an open environment or an indoor environment with a large flow of people, the video surveillance system in the classroom environment has unique environmental characteristics, that is, the flow of people is small, so the surveillance video can be obtained by video capture at a fixed position and a fixed angle. No need to consider tracking requirements. Through the analysis of the classroom environment, it is found that the first classroom is divided into regular classrooms and lecture rooms. The camera can be placed in the two corners of the classroom or in the middle of the classroom. After actual experiments, the latter was chosen for this design (taking into account the distortion of the side camera image), but under any of the abovementioned conditions, the camera position will not change. It should be noted that the camera’s angle of view is best to look down at the students so that the camera can more clearly capture the movement information of each student. But despite this, some students will still be occluded, and when the length of the classroom exceeds 5 m, it has a smaller resolution for the student targets relatively close to the back of the classroom. In order to compensate the influence of classroom scene characteristics on behavior recognition, a higher resolution camera should be used in the system design. The resolution of the Logitech C270 camera can reach 1920 × 1080, which can compensate for the problems of occlusion and low resolution of the students behind. In the classroom scene, the fixed background target detection method is not suitable for this system, because the student’s range of motion is narrow, and the placement of the object may change. Therefore, the images suitable for this design are selected from the COCO data set as the training data set.In the classroom scenario, when students have abnormal behaviors of playing with mobile phones, there will be obvious mobile phone objects appearing in the image, and the mobile phone object is the closest to a certain student object. The student object’s face is generally downward and part of the mobile phone object. The coincidence of the behavior and the duration of the behavior are generally more than 10 seconds. As for the behavior of sleeping in class, through image analysis, it is found that students’ sitting posture has changed significantly. The most fundamental feature is that they can hardly see any facial features, and this state will last for a long time. Student communication is an abnormal behavior of multiple people at the same time. Generally, there are two or more students participating, one after the other, or one on the left and the other on the right. When this behavior occurs, the student’s head will continue to rotate forward, backward, left, and right. The position of the student’s entire body has undergone a major change, which exceeds the area of activity during normal lectures. The fundamental purpose of this system is to realize the recognition of abnormal behaviors of students in the classroom, but the recognition of abnormal behaviors is based on target detection. In order to achieve a more accurate recognition of abnormal behaviors, both the recall and accuracy of student target detection should reach more than 90%, and the recall and precision of small target mobile phones should both reach more than 85%.The intelligent monitoring system designed in this article is mainly aimed at indoor collective activities such as meeting rooms, classrooms, and other places, and is mainly used to monitor the behavior of members under the situation of teachers teaching or leaders meeting. Because this type of scene is relatively small, the cost of its development must be low, so that the video surveillance system can be widely used. Based on the abovementioned reasons, this topic adopts a cost-effective embedded method for development. In addition, the use of wired network transmission makes networking easier in this type of scenario. The system should also have the performance of intelligent analysis, which can intelligently detect the relevant characters in the collected video. According to the characteristics and requirements of the classroom intelligent monitoring system, the system needs to meet the following requirements:(1)
The embedded camera must be able to realize a real-time collection of video data, video encoding, and transmission so that the terminal device can display video images without jams. Therefore, the embedded camera is required to collect video data at a higher sampling frequency.(2)
The embedded camera adopts digital, which is convenient for information transmission, storage, and processing, easy to connect with other communication equipment, and has strong compression potential, which can provide clearer video images. Due to its strong anti-interference ability, the image is less distorted.(3)
Data transmission is carried out through the wired network. Since the video data collected by the built-in camera must be transmitted through the network, the camera is connected to the router through a network cable in order to transmit the video data.(4)
With image analysis and processing capabilities, the collected video data is processed by behavior recognition algorithms, such as playing with mobile phones, sleeping, and communicating with each other. The behavior recognition algorithm detects the abovementioned behaviors and immediately transmits them to the teacher’s mobile APP terminal, and the APP receives messages and realizes the alarm in the form of sound effects, vibration, and a pop-up message box.(5)
The server-side and mobile APPs have permission restrictions. When the teacher watches the monitoring screen received by the server and APP, he needs to enter the password when registering the server and APP. Only when the password is consistent with the registration password can he access the server and APP. In addition, when the administrator logs in to the server to process the received video data, it also needs to be consistent with the password during registration.


## 4.2. System Overall Framework Design

In order to enable the system to have the scalability of the number of users and the flexibility of the layout of the hardware facilities, the system chose to access the local area network, transplant the camera application on the ARM9 development board, and send the collected data to the server, and the server performs intelligence on the video data. The mobile APP can receive the identified abnormal behavior video data after communicating with each other by accessing the server, as shown in Figure4.Figure 4
System frame diagram.According to the block diagram of the system, the system is mainly composed of an equipment terminal, router, internet, server intelligent detection module, and teacher’s mobile APP terminal. This system uses the ARM-LINUX platform to collect video images using the video data collection module, sends the collected data to the server discovery module, and transmits the identification results of abnormal behavior to the teacher’s mobile APP terminal.Embedded camera module: under the ARM-LINUX platform, the video data is obtained through the V4L2 driver framework. Use the x264 video encoding library to implement H.264 encoding. Router: the main function is to connect the embedded camera to the network. Server intelligent detection module: it mainly uses FFmpeg and H.264 to decode the video data transmitted from the device terminal, YOLOv3 intelligent detection, and stores the video data for administrators and teachers to view after logging in.Teachers’ mobile phone APP terminal: receive the detection results of the server, such as detecting abnormal behavior of students, transmitting the image data to the APP, generating sound effects, vibrations, and pop-up message boxes at the same time. After seeing the alarm information, the teacher will check the image data received by the APP so you can locate a specific student.


## 4.3. System Development Environment Design

The following are the specific steps to build the environment:(1)
Install the virtual machine on the PC and then install the Ubuntu 12.04 version of the LINUX operating system on the virtual machine(2)
After entering the LINUX system, copy the compressed package to the LINUX system(3)
Decompress the cross-compiler compressed package through commands on the terminal of the LINUX system(4)
Configure the environment variables of the cross-compiler and check whether the cross-compiler is installed successfullyAs we all know, for the LINUX system, there are very strict regulations and requirements on power consumption, functions, and costs. At the same time, it has a variety of different hardware interfaces to achieve strict management of the file system. In addition, it also has a series of different advantages, for example, easy to transplant, so the system has been generally welcomed by the industry.Bootloader is the first program executed after the embedded device is powered on, and can read and write flash, initialize SDRAM, initialize clock, initialize serial port, etc., to achieve the function of starting the kernel. This article uses U-Boot 1.1.6, the specific operations are as follows:(1)
Download the U-Boot 1.1.6 version from a website, transfer it to the LINUX system, enter the directory, and decompress it through commands(2)
Download the patch file that matches U-Boot 1.1.6, transfers it to the LINUX system, and open the U-Boot patch by command(3)
Configure and compile U-Boot is configured through commands, and then make compiles(4)
Use the programming tool to program the compiled u-boot.bin file into the ARM development board


## 4.4. Video Capture Module Design

In order to make full use of LINUX system resources, the camera application originally designed uses the V4L2 interface to realize the collection of image data. The collection of image data is mainly divided into three steps, the first is the initialization operation of the image data collection, the second is the application for the operating space memory, and the last is the collection operation of the image data.In the process of information transmission, the TCP/IP protocol is in a basic position, and it has a very common practical application in the internet field. For TCP/IP, it includes different levels, such as the hardware interface layer and transport layer. Through induction and summary, it can be concluded that TCP/IP has many advantages, and its basic structure is not complicated. This is because the protocol integrates the physical layer and data link layer of IOS to form a hardware interface layer and a session layer. And the presentation layer is included in the application layer.In the design, the communication protocol between the embedded development card and the server uses the connection-oriented TCP protocol. The TCP protocol is used because the TCP protocol has the following characteristics:(1)
TCP is a connection-oriented protocol. Before data exchange is realized between the development board and the application program of the server, the connection must be established through three handshakes, and the link is always occupied during the communication again. Until the data exchange is completed, the two parties will dismantle the link through four waves of hands.(2)
TCP has the characteristics of high reliability of data transmission. After the connection between the development board and the server is established, the development board will send new data to the server only after the server receives the correct data. If the development board does not receive the confirmation message from the server, the development board will retransmit the data until the server sends back the confirmation message or the message is sent over time.(3)
TCP is a full-duplex communication protocol. The development board and the server can send information to each other at the same time.(4)
TCP has the characteristics of sliding window control. TCP can determine the transmitted data traffic according to specific network conditions.


## 4.5. System Test

The peripheral equipment and embedded system of the core board, as well as the video data acquisition application program run on TQ2440. The specific configuration parameters of the core board are shown in Table4.Table 4
Embedded core board configuration parameters.
ConfigurationParameterCPU processorS3C2440, stablemaximum frequency 400 MHSDRAM64 MBNANDFLASH256 MBNORFLASH2 MBCore power1.25 VUSB cameraLogitech C270Embedded operating systemLINUX2.6.22.6 kernel versionBehavior detection server: considering real-time requirements, the classroom behavior detection and recognition part cannot be directly run on the mobile platform, so this article uses the upper computer-side server to implement the main algorithm part of the classroom behavior detection and recognition. The server uses i79700CPU and GTX1080GPU as the core computing unit and uses SSH to communicate with the mobile phone APP. The specific parameters are shown in Table5.Table 5
Behavior detection server configuration parameters.
ConfigurationParameterCPUi79700kGPUGTX1080System memory16G DDR4GPU memory8 GPower2 kWStorage500 G


## 5. Conclusion

In recent years, considerable progress has been made in the objective evaluation of voice quality testing, but there are still many problems to be resolved in the objective evaluation of voice quality, such as valuation principles and valuation methods. With the support of the VQIT (mobile internet voice quality test rating system)project, this article first introduces the needs analysis of remote English translation courses, focusing on the study of language quality assessment systems and measures to ensure language quality. When implementing requirements analysis, start with two parts: functional requirements analysis and nonfunctional requirements analysis, and then decompose each functional module. In the analysis of functional requirements, the main function points are divided into nine parts: video recording, video transmission, camera control, registration, video search, teacher query, student information, course query, and personal management. The analysis of nonfunctional requirements is described from four aspects: scalability, performance requirements, maintainability, and security.



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*Source: 3340999-2022-07-21.xml*