INNOVATION OF COLLEGE POP MUSIC

TEACHING IN TRADITIONAL MUSIC

CULTURE BASED ON ROBOT COGNITIVE-

EMOTIONAL INTERACTION MODEL

Hua Zhou*

School of Electronic Engineering, Xi’an Shiyou University, Xi’an, Shaanxi, 710065,

China

Shanghai Salvage, Shanghai, 201914, China

211122@126.com

Reception: 13/11/2022 Acceptance: 06/01/2023 Publication: 21/03/2023

Suggested citation:

Z., Hua. (2023). Innovation of college pop music teaching in traditional

music culture based on robot cognitive-emotional interaction model. 3C

TIC. Cuadernos de desarrollo aplicados a las TIC, 12(1), 200-220. https://

doi.org/10.17993/3ctic.2023.121.200-220

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ABSTRACT

Emotional computing and artificial psychology is a new research direction that has

received increasing attention in the field of harmonious human-computer interaction

and artificial intelligence and is also a new intersection of mathematics, information

science, intelligence science, neuroscience, physiology, psychological science and

other multidisciplinary intersection. The current problems and drawbacks in the

teaching of popular music in colleges and universities, and the search for methods

and measures to reform and innovate popular music education in colleges and

universities are the difficulties of current music teaching work. In this paper, we try to

apply a robot cognitive-emotional interaction model to college pop music teaching,

and establish an emotional interaction model based on reinforcement learning with the

help of cognitive-emotional computing of human-computer interaction, to be able to

integrate emotional interaction in pop music teaching and to make an accurate

emotional analysis of students' singing effect. Different from traditional music teaching

methods, the robot-based cognitive-emotional interaction model established in this

paper can establish an innovative teaching model for college pop music teaching and

optimize the teaching effect.

KEYWORDS

Robot cognition; Emotional interaction; Popular music; University teaching;

Optimization and innovation

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PAPER INDEX

ABSTRACT

KEYWORDS

1. INTRODUCTION

2. EMOTIONAL DESCRIPTION AND EMOTIONAL INTERACTION

2.1. Affective description model

2.1.1. Dimensional sentiment description model

2.1.2. A discrete sentiment description model

2.2. Cognitive-emotional computing for human-computer interaction

3. THE COGNITIVE-EMOTIONAL INTERACTION MODEL FOR ROBOTS

BASED ON REINFORCEMENT LEARNING

3.1. Cognitive-emotional computing based on reinforcement learning

3.2. Emotional interaction model based on reinforcement learning

3.2.1. Status

3.2.2. Behavior

3.2.3. Discount factor

3.2.4. Rewards

3.2.5. Strategies

3.2.6. Model Optimization

3.2.7. Emotional interaction process simulation

3.3. Robot Emotional State Update

4. INNOVATION OF POPULAR MUSIC TEACHING IN COLLEGES AND

UNIVERSITIES

4.1. Popular music and traditional music culture

4.2. The Role of cognitive-emotional interactive robots in music teaching

5. CONCLUSION

DATA AVAILABILITY

CONFLICTS OF INTEREST

REFERENCES

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1. INTRODUCTION

As human communication is natural and contains multiple emotions, it is also

natural to expect computers to have the ability of emotional cognition in human-

computer interaction. How to enable computers to recognize, understand, and

generate human-like emotions has received widespread attention from disciplines

such as computer science, brain science, and psychology, and has given birth to the

intersection of cognitive-emotional computing [1-3]. As an important way to reflect the

application value of artificial intelligence technology, the research of human-computer

interaction systems has received common attention from academia and industry, and

human-computer interaction products have gradually entered people's daily life.

Influenced by the great satisfaction of material needs, people have begun to desire

and pursue the sense of fit brought by human-robot interaction at the emotional level,

and hope that robots can have the cognitive-emotional computing ability to generate

advanced anthropomorphic emotions while satisfying daily interaction needs [4-5].

The study of cognitive-emotional computation for robots is the key to realizing

advanced human-robot interaction technology with organic integration of emotions,

which has important practical significance and high research value. The applications

of robotic cognitive-emotional interaction models are also becoming more and more

multifaceted. In the field of popular music teaching in colleges and universities, this

paper is devoted to making innovations with the help of cognitive-emotional interaction

robots for its disadvantages such as backward teaching model, preaching by the book

and lack of vitality [6].

Intelligent human-robot dialogue systems, as an intuitive manifestation of

intelligence in artificial intelligence technology, have become an important research

component for achieving natural and harmonious human-robot interaction. In recent

years, to improve robot anthropomorphism in human-robot interaction systems and

create a harmonious and friendly human-robot interaction environment, researchers

have conducted a lot of research around cognitive-emotional computing of robots in

open domains, and numerous cognitive-emotional interaction models with important

reference values have emerged. The literature [7-8] proposed an affective interaction

model based on a guided cognitive reappraisal strategy to emotionally regulate

external affective stimuli and promote the positive affective expression of the robot to

some extent. In the literature [9-10], the cognitive emotion model of the robot is

integrated into the smart home environment, and the cognitive reassessment strategy

guided by positive emotion is obtained by optimizing and analyzing the cognitive

emotion model of the service robot in the smart home environment using simulated

annealing algorithm, and the probability of transferring emotional states is updated

based on the cognitive reassessment strategy. Literature [11-12] proposed the multi-

emotion dialogue system MECS, which tends to generate coherent emotional

responses in dialogue and selects the most similar emotion as the robot response

emotion. The literature [13-14] proposes emotional chat machines that can produce

appropriate responses not only in terms of content relevance and syntax but also in

terms of emotional coherence. In response to the teaching of popular music in

colleges and universities, the American Education Act explicitly proposes that music

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education in schools should break with tradition and no longer be limited to the

teaching and development of traditional resources, but expand to all aspects of

popular music and contemporary music [15]. German universities believe that both

elegant music and modern music should be actively drawn from the advantageous

factors of these musicians in the teaching and development of universities so that

students can have a richer musical vision from them and can be exposed to richer

musical content [16]. With the continuous development of music education, the

application of popular music in college education has become more and more

extensive, making the music classroom in colleges and universities more colorful and

further highlighting the function and value of popular music teaching.

Establishing and maintaining social connections with others is a basic human need

in interpersonal relationships, and when users use more anthropomorphic robots as

partners and establish relationships with them, they have a better willingness to

continue interacting with these robots for a longer period than with those robots that

are more rigid in their expression. As robots become more anthropomorphic, the user

experience increases and the trust and dependence on the robot increase. In this

paper, we investigate the application of emotional robots to the teaching of popular

music in universities. First, we quantify the emotion analysis of human-robot

interaction by multi-dimensional emotion description, which makes it possible for the

robot to compute emotion. Then, the emotional input and output of human-robot

interaction are simulated based on reinforcement learning algorithms, to establish its

complete cognitive-emotional interaction model. Finally, the model is practiced in

college pop music teaching, and a pop music teaching model that makes innovations

in traditional music culture is proposed.

2. EMOTIONAL DESCRIPTION AND EMOTIONAL

INTERACTION

2.1. AFFECTIVE DESCRIPTION MODEL

The ultimate goal of robotic cognitive emotion computing can be interpreted as

giving human-like artificial emotions to robots in interactive systems by simulating

human emotion processing to build trust between humans and machines. The main

research content includes three parts: emotion recognition, emotion modeling and

emotion understanding. Due to the complexity and abstraction of actual emotion,

before establishing the robot cognitive emotion interaction model, it is necessary to

recognize and quantify the emotion of the user interaction input content, and convert it

into an emotion state vector that can be recognized and processed by the computer,

and at present, according to its different ways of emotion description, the methods for

emotion recognition and quantification The current methods for emotion recognition

and quantification can be divided into dimensional emotion description models and

discrete emotion description models.

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2.1.1. DIMENSIONAL SENTIMENT DESCRIPTION MODEL

The dimension-based emotion description model describes emotions as coordinate

points in a state space composed of Cartesian product operations, each dimension in

the state space corresponds to a psychological attribute of emotion, and the

magnitude of the value on the dimension reflects the strength of the emotional

characteristics corresponding to the psychological attribute, and the emotion

description capability of this space covers all emotions [17-18]. In other words, that is,

there is a one-to-one mapping relationship between the emotions that exist in reality

and the coordinate points in the state space. At the same time, the similarities and

differences between different emotions can be quantified and analyzed by calculating

the distance between coordinate points. The basis of emotion calculation is to find this

mapping dimensional theory, which regards the transition between different emotions

as a continuous and smooth state transfer process. The dimensionality-based model

of emotion description has received a lot of attention from scholars because it

combines the characteristics of continuity and complexity of human emotion

distribution. According to the different ways of dividing emotional attributes in

psychology, dimension-based emotion description models have one-dimensional, two-

dimensional, three-dimensional and more multi-dimensional spatial theories.

Currently, the widely used dimensional description models are the two-dimensional

activation-valence spatial theory (AV), where the Arousal axis is the activation

dimension, representing the degree of pleasantness-unpleasantness, and the Valence

axis is the valence dimension, representing the degree of agitation-calmness. The

three-dimensional Pleasure-Activation-Dominance (PAD) spatial theory, in which the

Pleasure axis is the Pleasure dimension, representing the degree of positive and

negative affective states, and the Arousal axis is consistent with the Arousal axis in the

AV spatial theory, representing the individual's level of neurophysiological activation.

The AV spatial theory is shown in Figure 1, from which we can see that the emotional

labels that exist in daily life can be mapped to coordinate points in space, and the

magnitude of the corresponding coordinate values in each dimension varies according

to the strength of each emotional attribute.

Figure 1. Activation Degree-Validity emotional space

pleased

insterested

happy

excited

alamed

JoyAnger

annoyed

nervous

angry

afraied

terrified

furious

Arousal

-1

Valence

content

relaxed

serenecalm

NeuralSadness

sad

depresed

boredsleepy

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2.1.2. A DISCRETE SENTIMENT DESCRIPTION MODEL

Discrete models of emotion description describe emotions in the form of a finite

number of adjective labels and are widely used in people's daily lives, as well as in

early computational research on emotions. Rich linguistic labels can describe a large

number of affective states, but which classifications are of higher research value? This

question can be attributed to the classification of basic affective states based on

adjectival labels [19-21]. In general, the categories of emotions that can cross different

races and cultures and are shared by humans and socially oriented mammals are the

basic emotions. Table 1 lists the definitions and classifications of basic emotions by

different scholars.

Table 1. Definition of basic emotions by different scholars

In summary, considering the rich and strong emotions of participants in open-

domain HCI systems, the continuous model based on the PAD emotion space is

chosen for the quantitative analysis of emotions in the open-domain cognitive emotion

study of robots [22-24]. In closed-domain HCI systems, the emotions of the

participants are simpler, and the discrete emotion model is chosen to classify the

emotions into positive, negative and neutral when studying the cognitive emotions of

robots for the closed domain.

2.2. COGNITIVE-EMOTIONAL COMPUTING FOR HUMAN-

COMPUTER INTERACTION

In the long history of robotics research, researchers have focused more on the

design and manufacturing of robots, control systems, drive systems, and content

representation, until the introduction of "affective computing" and the gradual shift of

Scholars Basic emotions

Weiner, Graham Happiness, Sadness

Watson Fear, Love, Rage

James Fear, Grief, Love, Rage

Panksepp Anger, Disgust, Anxiety, Happiness, Sadness

Ekman, Friesen,

Ellsworth Anger, Disgust, Fear, Joy, Sadness, Surprise

Fridja Desire, Happiness, Interest, Surprise, Wonder, Sorrow

McDougall Fear, Disgust, Elation, Fear, Subjection, Tender-emotion,

Wonder

Plutchik Anger, Interest, Contempt, Disgust, Distress, Fear, Joy,

Shame,

Surprise

Tomkins Anger, Interest, Contempt, Disgust, Distress, Fear, Joy,

Shame,

Surprise

Arnold Anger, Aversion, Courage, Dejection, Desire, Despair,

Dear, Hate,

Hope, Love, Sadness

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robotics research to intelligent robotics, robot cognitive emotion research has received

increasing attention [25-27]. The study of robot cognitive emotion has received

increasing attention from researchers [25-27]. Emotion is a subjective response to a

valued relationship, which is simply the process of perceiving the impact of external

values on oneself, and to facilitate an intuitive understanding of this process, a triad

can be used to represent cognitive affective computing formally:

SC = (S, C, W) (1)

where S denotes the set composed of different types of information carriers such

as text, speech and pictures. Different carriers contain different emotional features,

and there are large differences between the representations of different features.

C denotes the set composed of different sentiment categories. C can denote a

discrete set of sentiment states composed of several basic sentiment states, or a

spatial set of sentiment states composed of different sentiment dimensions.

W denotes the set composed of different emotional trait intensities, and the

intensities can be initially quantified into basic high, medium and low levels, or further

subdivided.

Emotional features combined with intensity features form the core of cognitive

sentiment computing. Cognitive sentiment computing first identifies and quantitatively

analyzes the data features related to objective things extracted from different

information carriers. Secondly, the affective features in the data features are

calculated under different polarity dimensions to achieve the subjective affective

perception of objective things. Finally, it is fed back to the participant with an

appropriate sentiment expression. The cognitive sentiment computation can be further

described as a combination of state space composed of S, C and W by Cartesian

product operation, i.e:

SC = S × C × W (2)

In recent years, the emergence of smart speakers and other devices has largely

promoted the productization of human-robot interaction systems. In the process of

cognitive-emotional modeling of robots, existing research has achieved better results

in the extraction and recognition of emotional features. With the development of

intelligence in various industries, research in AI-related fields has been greatly

promoted, and its ultimate development goal is to establish intelligent bodies with the

ability to observe the environment and to think and decide independently.

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3. THE COGNITIVE-EMOTIONAL INTERACTION

MODEL FOR ROBOTS BASED ON REINFORCEMENT

LEARNING

3.1. COGNITIVE-EMOTIONAL COMPUTING BASED ON

REINFORCEMENT LEARNING

In the process of human emotion generation, individual emotional state responses

are not only related to external emotional stimuli but also related to their emotional

states and emotional interaction motives. When performing affective state response,

we should not only consider the influence of contextual multi-round interaction context

on the probability of transferring the current affective state but also consider the

influence of the current affective state response on the subsequent interaction

relationship. Therefore, to effectively carry out robot emotional strategy learning, this

paper proposes to use reinforcement learning features to establish the correlation

between contextual multi-round emotional state and current response emotional state,

and perform cognitive emotion computation for the robot, and the computational

framework is shown in Figure 2.

Figure 2. The framework of robotic affective computing

To facilitate the implementation of participant sentiment state tracking, sentiment

quantification and state evaluation are performed on the interactive input content. In

this paper, we quantify the sentiment of the interactive input content and obtain its

corresponding sentiment value in the PAD continuous sentiment

space. Secondly, the interaction sentiment value vector is evaluated in terms of

state, and its sentiment state vector under the action of six basic sentiment

states in the PAD continuous sentiment space is obtained. The emotion state

evaluation function is defined as:

(3)

(4)

Current emotional

state

Emotional state

transfer probability

External emotional

stimulation

The next moment

of emotional state

Responding to

emotional output

Cognitive-emotional

computing based on

reinforcement learning

Ei= (p,a,d)

I(Ei)

I(Ei)=[i1,i2,i3,i4,i5,i6]

ij=1/h

∑j=1

61/hj

,hj≠0

1= 0,i2= 0,1,⋯,ij= 1,2,⋯,i6= 0

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(5)

where

denotes the interactive input sentiment value. j = 1, 2, …,6 denotes the

six basic sentiment states of happy, surprised, disgusted, angry, fearful, and sad,

respectively; denotes the sentiment value corresponding to basic sentiment j.

denotes the covariance matrix of the clustering region of basic sentiment j. denotes

the distance between and

. then denotes the assessed value of the affective

state of under the action of .

3.2. EMOTIONAL INTERACTION MODEL BASED ON

REINFORCEMENT LEARNING

The reinforcement learning model is based on the principle that intelligence, in its

current state, performs a behavior to interact with the environment and enters a new

state, while obtaining the corresponding immediate reward from the environment, and

then evaluates this behavior according to the reward, and the reward value increases

for behaviors favorable to goal achievement and decays for behaviors unfavorable to

goal achievement, and this process continues to cycle until the termination state

[28-31].

3.2.1. STATUS

The state

indicates the emotional state in which the intelligent body is, which is

usually given by the external environment. To reduce the granularity of emotion

division and increase the continuity and delicacy of robot emotion expression, the

PAD continuous emotion space containing 151 emotion states is taken as the emotion

state space of the intelligent body in this paper, and the emotion state vector of each

emotion state in the space under the action of six basic emotion states is taken as the

possible interaction input response emotion states.

3.2.2. BEHAVIOR

Behavior

denotes an action executed by the intelligent body in the interactive

response process when selecting the next round of response emotional state with the

search space of the emotional space size. The activity process of the intelligent body

in the emotion space is the Markov transfer process between the emotional states in

the emotion space.

3.2.3. DISCOUNT FACTOR

The discount factor γ

can be used to calculate the future reward decay of the

cumulative rewards of a state sequence when the environment is stochastic. In this

paper, we consider that the more distant the future moment is from the current

session, the smaller the effect of future rewards on the satisfaction used to measure

the affective state of the next round of sessions. Its value is between 0 and 1. The

hj= (Ei−Ej)Cj(Ei−Ej)T,j= 1,2,⋯,6

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greater the importance of future rewards is considered, the greater the value of , and

vice versa the smaller the value of .

3.2.4. REWARDS

The reward r can be used to measure the future satisfaction of the obtained

affective state after the intelligent body performs the corresponding action

. Both

sides of the human-robot interaction have certain emotional motivations during the

interaction. Based on the principle of interpersonal attraction in social psychology, the

emotional motivation of robot interaction is set to achieve a certain degree of

emotional affirmation, emotional guidance and emotional empathy for the participant,

and the emotional reward function is constructed accordingly.

Similarity emotional reward function: Considering the process of interpersonal

interaction, people often hope that the other party can produce similar emotional

responses to themselves, to achieve the emotional affirmation of the participant, the

cosine similarity is calculated to measure the similarity function between the

emotional state vectors as

(6)

Positive affective reward function: Considering the process of interpersonal

interaction, people will achieve some kind of emotional guidance to others by

adjusting their emotional expression state. Therefore, to achieve emotional

guidance for participants, this paper increases the participants' willingness to

interact by setting the robot's emotional positivity guidance. In fact, the higher the

positivity, the better, especially when the participant's emotion is negative, it may be

counterproductive. The synergistic effect of positivity and similarity can effectively

solve the problem of over-guidance. Therefore, in this paper, the positivity of the

response emotional state vector is calculated as

(7)

Empathic affective reward function: Consider the process of interpersonal

interaction in which interpersonal attraction is not only related to the similarity

between individuals but is also influenced by complementary relationships with

each other. Complementary relationships are influenced by the fact that people

sometimes tend to prefer people who can complement them in some way. In

emotional interactions, this can be interpreted as the expectation that the other

person has empathy and resonates with them in terms of emotional expression.

Therefore, this paper measures emotional empathy by calculating the

interrelationships between emotional state vectors

( ) ( ) ( )

( ) ( )

1 1

,k k

k k

I E I E

r S E E

I E I E

⋅

= =

( ) ( )

( )

2 1 1

k k j j

r P E P I E l i

+ +

= = = ∑

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(8)

For behavior

, the final reward it receives is the weighted sum of the above 3

reward measures

(9)

where , , and are the corresponding weight parameters, respectively.

3.2.5. STRATEGIES

The policy P is used to represent the probability distribution corresponding to when

the intelligence chooses the next emotional state in the current state and can be

expressed by the formula

(10)

The initial value is the initial transfer probability between affective states. The model

is generally optimized using a strategic gradient algorithm, so its value is related to the

future reward value available for selecting the next affective state, with a greater

probability of occurrence for actions that receive a large future reward value and

correspondingly a smaller probability of occurrence for actions that receive a small

future reward value.

3.2.6. MODEL OPTIMIZATION

The model update training is achieved by parameterizing the policy through the

policy gradient algorithm, to maximize the future cumulative reward expectation by

optimizing the model parameters. Therefore, the objective function is to maximize the

expected value of future rewards, defined as

(11)

where denotes the reward value obtained by acting

in the state

; then the gradient is updated using the likelihood ratio technique

(12)

Finally, the parameter is updated using the obtained gradient values

(13)

( ) ( ) ( )

( )

( ) ( )

( )

3 1 2

, log |

1log |

k k k

r M E E P I E a

rank E

P a I E

rank E

= = +

( )

1 1 2 2 3 3

R a I E r r rα α α= + +

α1

α2

α3

( ) ( )

( )

( | ) |

RL k k

a s P I E I Eπ

( )

( ) ,

RL k k

RH a

L E R a I Eθ

⎡ ⎤

=⎢ ⎥

⎣ ⎦

∑

Rk(ak,I(Ek))

I(Ek)

( )

RL 2

( ) log , ,

k k k k

L P a R a

θ θ

∇=∇

∑I E I E

( )

new old

θ θ β θ= + ∇

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When the cumulative reward expectation is maximized, the sentiment state

corresponding to the resulting optimal policy is the optimal response sentiment state

for the interaction input.

3.2.7. EMOTIONAL INTERACTION PROCESS SIMULATION

This chapter uses two Agents to simulate the emotional interaction process

between the Agent and the external environment by continuously interacting with each

other. The interaction process between two Agents can be described as follows: first,

Agent 1 is given a random emotional state

, and then Agent 1 converts it into an

emotional state vector

through emotional evaluation and then transmits this

vector to Agent 2 as input, after which Agent 2 converts the corresponding response

emotional value evaluation into an emotional state vector

back to Agent 1,

and this process is repeated until the maximum number of interaction rounds is

simulated. The interaction goal is to be able to select the optimal emotional state with

the maximum future reward under the current interaction input emotional state. The

emotional interaction process between Agents is shown in Figure 3.

Figure 3. Emotional interaction process

The distance of spatial distance is used to map the similarity between different

affective categories in the affective space. The transfer probability between emotional

states differs with different distances and similarities between categories. The closer

the distance, the greater the state transfer probability; conversely, the farther the

distance, the smaller the state transfer probability. Therefore, to facilitate the

calculation and analysis of the emotional states in response to external emotional

stimuli, this chapter uses the top

emotional states in space that are closest to the

Euclidean distance from the external emotional stimulus point as the candidate

emotional states for each round of Agent interaction.

3.3. ROBOT EMOTIONAL STATE UPDATE

In this paper, the optimal response emotional value of the robot in continuous

emotional space is calculated by combining the six basic emotional values with the

probability of emotional state transfer obtained by the robot after being subjected

to external emotional stimuli to achieve the emotional state transfer of the robot in

(E1)1

I(E2)

E1E3E5

E2E4E6

Agent 1 Agent 1 Agent 1

Agent 2 Agent 2 Agent 2

( )

I E

( )

I E

( )

I E

( )

I E

( )

I E

Pk+1

Ek+1

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continuous emotional space [32-33]. First, assuming that the optimal response

emotional state vector obtained from the reinforcement learning model corresponds to

a strategy

, the probability of transferring the response emotional state to the six

basic emotional states based on the user input can be obtained as:

(14)

Second, the

rounds of robot response emotional state transfer probability

are updated by combining the rounds of robot emotional state transfer

probability and the rounds of interactive user input optimal response emotional

state transfer probability with the following equation:

(15)

(16)

where

represents the confidence level of transferring the interaction input

response emotional state to the 6 basic emotional states [34-35]. The resulting

transfer probability

is then normalized to obtain the transfer probability of the

-round interactive robot response emotional state as:

(17)

Finally, the coordinate position of the

-round robot optimal

response emotional value

in the emotional space is calibrated based on the

obtained robot emotional state transfer probability

, which is calculated as

follows:

(18)

(19)

Based on the above conditions, the interaction input content emotion is quantified

and evaluated based on the PAD emotion space, the user and robot emotion

generation process in the human-robot interaction system is modeled using

reinforcement learning, the long-term correlation between the current interaction input

emotion state and the contextual long-term emotion state is established, and the

model parameters are optimized and updated by maximizing the reward value

( )

1 1 2 3 4 5 6

[ (1), (2), (3), (4), (5), (6)] , , , , ,

k HR p p p p p p

P E E P P P P P P i i i i i i

⎡ ⎤

= = ⎣ ⎦

∣

k−1

Pk−1

k−1

Pk+1

P(Ek+1 |Ek

HR)

1 1

( ) ( ) ( )

k k

R RH j

P j P j c P j

+−

= +

( )

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expectation, and finally, the optimal response emotion state corresponding to the

obtained user input is realized by updating the transfer probability of k + 1 rounds of

robot emotion state, and the optimal response emotion state of the robot in the

continuous emotion space can be obtained.

4. INNOVATION OF POPULAR MUSIC TEACHING IN

COLLEGES AND UNIVERSITIES

4.1. POPULAR MUSIC AND TRADITIONAL MUSIC CULTURE

In the new era, the traditional music teaching mode is increasingly unable to meet

the continuously growing spiritual and cultural needs of students, and pop music

teaching in colleges and universities needs to develop in a diversified direction so that

students can express their real emotions more intuitively through learning and

mastering pop music. Pop music teaching in colleges and universities mainly presents

the following characteristics.

1. Both artistic and popular

The teaching of popular music in higher education should be fully based on art

education, but on top of that, it should also ensure that it is more significantly

popular to ensure that students can understand and accept it in the teaching

process. In the teaching of pop music in colleges and universities, it is because of

the artistic and popular characteristics that students are more likely to understand

and sing it. In conclusion, the teaching of pop music needs to be both popular and

artistic. In the process of development, universities should firmly grasp the pulse of

the development of art education, distinguish it from other types of music, and make

the teaching height continuously improve through more professional control.

2. Tradition and fashion intermingle

The content of popular music teaching in colleges and universities have both

characteristics of tradition and fashion. Tradition refers to the unique ideological

nature of popular music over the years of development, which can, to a certain

extent, pass on the culture and values of the era in which it lives, and has a

stronger ideological appeal and influence, and that influence is fundamental to the

continuous development of popular music, as well as being a characteristic that

needs to be observed in the process of building popular music.

3. Integrating and diversifying at the same time

The reason for this is that the curriculum should take into account the

comprehensive quality and development requirements of students, and needs to

have a certain degree of comprehensiveness, but also needs to add corresponding

special practical courses to fully ensure that students can have a high level of

comprehensive quality. In addition, pop music was established late, and it needs to

integrate instrumental music, vocal music and music management in the actual

teaching process to improve its perfection and become one of the more mature

music majors, so it has a more significant comprehensive.

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4. Unity and independence are common

Popular music in colleges and universities can also fully reflect the unity and

independence of music in the teaching process. Unity mainly refers to the teaching

process, teachers set the subject construction plan, curriculum system design and

other content based on the curriculum design and other music majors' teaching

methods. Based on this, the construction of each music major can maintain a high

degree of unity, and the basic construction steps in the implementation process will

not have a high degree of deviation, and can jointly promote the orderly

development of the overall music major construction.

5. Inheritance and innovation co-exist

Heritage and innovation is one of the characteristics of teaching popular music in

colleges and universities, and also the purpose of teaching popular music in

colleges and universities. Artistic, popular, traditional and fashionable belong to the

characteristics of pop music itself, while inheritance and innovation are based on

the artistic level for enhancement. The inheritance of teaching and professional

construction of popular music in colleges and universities is mainly reflected in a

variety of musical concepts, music construction and inheritance of traditional art

music, in addition to the inheritance and expansion of the way of professional

construction of conventional music and the development of the experience of art

education in colleges and universities. Compared to other contents, the way of

building conventional music is the inheritance of methods, while the way of

developing popular music mainly targets the innovation level and is the most

innovative compared to other characteristics. In actual teaching, the combination of

inheritance and innovation can fully reflect the developmental qualities of the

professional construction of popular music.

4.2. THE ROLE OF COGNITIVE-EMOTIONAL INTERACTIVE

ROBOTS IN MUSIC TEACHING

The form of education is an inherent requirement of educational work, and it is also

an inevitable requirement of society and the times for educational work. Educators in

general should make more innovative and diligent research in teaching methods and

approaches, work with diversified, multi-level and multi-angle music teaching

methods, and incorporate the healthy parts and excellent parts of the many traditional

music forms in China into the teaching contents, meanwhile, they should widely

absorb the advanced teaching experiences at home and abroad, and show the many

excellent music forms to students in the teaching process, so that they can It is also

beneficial to the promotion and growth of these excellent forms of music to have more

choices while relying on popular music.

Cognitive-emotional interactive robots can be used to create an intimate teaching

atmosphere of mutual respect, mutual trust and mutual help, thus making the teaching

relationship more pleasant and harmonious. In such a relaxed and pleasant

atmosphere, students' self-confidence will be improved and they will develop lively,

cheerful and positive psychological qualities, which can not only relieve some of the

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more common bad emotions such as nervousness and anxiety in the college pop

music classroom, but also increase the fun of learning pop music and turn pop music

learning into a happy and joyful thing, and face the future with a more positive They

will be able to face the present and future with a more positive attitude.

The high school pop music classroom is required to teach specific one-on-one

lessons. The teaching process begins with listening to the student's singing of the

song, then finely identifying any vocal problems that arise and giving the student

specific instructions on how to perform the song. The student then imitates and sings

according to the Emotion Robot's analysis and suggestions, while at the same time,

the Emotion Robot accurately listens to see if the sound the student is making is up to

snuff, similar or consistent with what the Emotion Robot expects, and guides the

student to find the correct vocal method for singing.

The classification results of the cognitive-emotional interaction robot are shown in

Figure 4 by recording and analyzing 15 pop music singing performances of multiple

students.

Figure 4. Vocal and musical classification

Figure 4 gives a detailed comparison of the intermediate results of classification

based on the SVM classifier and the final classification results after low-pass filtering,

as well as the percentage of human voices in each song. It can be seen that the SVM-

based classification method and the post-processing filtering mechanism are effective.

The first two songs are rap-style songs, which show strong linguistic properties, so

MFCC combined with the classical model of traditional speech recognition like SVM

has got good results on them. The classification effect of the 4th song and the 12th

song is poor, the former as a Cantonese song has a great difference in vocal

characteristics from the training data; the latter female voice is more difficult to

distinguish from the music and cannot effectively capture the difference between the

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human voice and music. In contrast, the classification method of the cognitive-

emotional interactive robot is effective in terms of the overall classification effect.

The cognitive-emotional interaction robot was also able to identify the emotion of

the student's pop music singing to compare it with the emotional tone of the original

song, and the results are shown in Figure 5.

Figure 5. Emotion recognition in music singing

It is not difficult to find that data points of various types of emotions are intertwined

on the vector space due to the existence of errors in the data, and since the projection

process is a linear transformation, the fusion of data with each other has a small

impact on the accuracy of the robot cognitive-emotional discrimination based on

reinforcement learning. Based on the basic rhythmic information, the cognitive-

emotional interactive robot can perform the initial recognition of basic emotions.

Although the recognition results need to be further improved, this conclusion can be

applied to musical emotion recognition systems where recognition accuracy

requirements are not very stringent. Further, we can use a neural network approach to

perform more accurate emotion information recognition using a more adequate set of

feature parameters and apply it to improve the recognition rate and robustness of the

music recognition system and achieve more human and intelligent human-robot

emotion interaction.

5. CONCLUSION

The current intelligent development of human-robot interaction systems has

reached a high level, and robotic cognitive-emotional computing has received more

and more attention from researchers as an important research component of its

intelligent development. In this paper, we carry out research work on the problems of

robot cognitive-emotional research in open-domain and closed-domain systems,

propose a robot cognitive-emotional interaction model based on reinforcement

learning, and apply the model to the innovation of university popular music teaching in

traditional music culture. The integration of Chinese traditional music culture in pop

music professional education can well cultivate students' traditional music culture

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literacy and traditional music culture communication ability. The combination of

modern information technology and artificial intelligence technology is in line with the

development trend of pop music, and the efficient combination of the two can inject

more contemporary vitality into pop music, stimulate the musical creativity of college

students, and improve the students' artistic professionalism and social

competitiveness.

DATA AVAILABILITY

The data used to support the findings of this study are available from the

corresponding author upon request.

CONFLICTS OF INTEREST

The author declares that there is no conflict of interest regarding the publication of

this paper.

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