In recent years, digital music resources have grown rapidly and have been widely used in teaching. Teachers began to use digital music resources to assist teaching and achieved good learning results. However, with the increasing number of digital music resources, effectively managing these resources has become an urgent problem in music-assisted teaching . An important function of digital music resource management is resource retrieval. Scholars have done extensive research on music resource retrieval methods and proposed content-based music retrieval at home and abroad. The method realizes retrieval methods such as content-based music retrieval, humming-based music retrieval, and melody-based music retrieval by recording the acoustic signal, rhythm, or melody. The music itself is very emotional. Therefore, the emotion-based digital music retrieval method conforms to the essential characteristics of music and is also the user’s demand. However, emotion cannot be accurately measured and is highly subjective. So how to quantify emotion is the core issue of music emotion retrieval technology. Fuzzy mathematics is some fuzzy concept that people encounter in daily life. At present, the principles of fuzzy mathematics are the most widely used in the two aspects of object classification and individual evaluation . At the same time, fuzzy mathematics has applications in financial management, artificial intelligence, medical diagnosis and treatment, environmental monitoring, and other fields. Fuzzy mathematics can be applied to the fields related to classification and degree judgment because the result of the membership function can express the relative degree of a certain entity belonging to a certain type. This also reflects the multi-faceted nature of the individual. If the concept of fuzzy mathematics is introduced into music emotion retrieval, people can objectively describe how sad or happy a piece of music is. On this basis, an emotional space compatible with fuzzy mathematics is found from the classification model of music emotional classification. The article uses membership functions to classify selected music fragments into different emotion types based on fuzzy mathematics to achieve music emotion retrieval. Finally, experiments have verified the effectiveness of this retrieval method.
In addition to music, including rhythm, pitch, tempo, lyrics, and other factors related to its composition, it also includes extended factors such as emotion, visual image, and imaginary space. In a sense, the emotion of the music is objective. Because the emotion of music mainly reflects the subjective emotional experience of the creator when creating it, this emotional experience has been positioned by the creator for the listener . Different listeners have different emotional experiences of the same music in different mental states, but this difference will not be very different from the emotional state of the original creator. In general, the objectivity of the emotional tone of the music is a major premise for us to achieve fuzzy retrieval.
The currently recognized emotion classification models are Ressell’s emotion space and Thayer’s improved emotion space in the classification of music emotions. The “A” in Russell’s emotional space refers to activation, called “activity” in psychology. We use it to measure whether people’s emotions are high. “V” means valence. It is called “inducing force” in psychology. We use it to measure whether people feel happy. Russell found that people’s emotional preferences have an “inverted U-shaped” relationship with A, and V has a linear relationship with people’s emotional preferences . At the same time, many emotions can also be combined by the two-dimensional value of Please Nt Ness and Activation. Thayer divides the emotions of music into four major categories: joy, sadness, Clam, and anxiety. Here we use Thayer’s emotion vector space model and retain the functional relationship between A and V and emotion described by Russell and their combined characteristics.
In this search method, the search terms we provide to users are happy, anxious, sad, and peaceful. The process of fuzzy retrieval mainly involves the representation of fuzzy sets, the determination of membership functions, the ranking of retrieval results and the extraction of retrieval keywords. Suppose domain
The abscissa Valence is abbreviated as V, and the variable name is denoted as v. The range from left to right is [−0.33, 0.33]. The ordinate activation is abbreviated as A, and the variable name is denoted as A. The value range from bottom to top is [−0.26, 0.26].
A’s value is close to 0.26 when the emotion is very exciting, such as “excitement.” When the emotion is generally pleasant, the value of A is around 0.1. When people’s emotional preference is in the emotions of fear, anger, and tension, A’s value is 0.23~0.16. When in sadness, A’s value is mainly in the region of −0.03~−0.20. When the A value is between −0.23 and −0.16, it can be concluded that the emotional state is “calm.”
First, determine the corresponding membership function when the emotion is “happy” and mark its membership function as
A similar method can be used to obtain the membership function
The V variable has a linear relationship with people’s emotional preferences. When the value of V increases from small to large, the order of the emotional state is sad, angry, calm, and happy. The V variable also has 4 fuzzy sets: happy, anxious, sad, and calm . We denote its membership functions as
When the value of V is above 0.09, the emotion of “happy” will be more obvious as the value of V becomes larger. When the V value is around 0.1, the emotion tends to be calm. When V is between −0.16 and −0.05, the probability of “tension, anger, anxiety” is very high. When the V value is less than −0.16, the sentimental emotions will become more obvious as the V value becomes smaller.
The study found that A21 is a relatively large function. Above the value of 0.17, “happy” or emotions similar to happiness will appear. Then determine the domain of membership of 0 and 1. The membership domain with a degree of 0 is [−0.33, 0.09). The membership domain with a degree of membership of 1 is (0.19, 0.33]. The range of the transition zone is [0.09, 0.19], and the shape of the transition zone is linear. So finally, the membership function of the trapezoidal distribution is selected, and the function
Also, according to the above method, the function expressions of
First, select experimental samples. We randomly selected 90 music samples. These music samples include 40 classic European and American music and 50 classic Chinese music . The repertoire we have selected also includes various types of folk songs, country songs, pop music, rock music, classical music, etc.
Secondly, determine the membership value of each piece of music. First, determine the A and V values of each sample music, and then use the membership function to obtain the membership degrees of the above four emotions according to this basic judgment. A is 0.03, and V is 0.17. We come to its
Build a simple retrieval system again. The establishment of the retrieval system is divided into two parts: the establishment of the data table and the establishment of the retrieval page . The data table for fuzzy search is music1. It contains 5 attribute fields: title, happy, anxiety, sad, and Calm.
We use traditional methods to estimate the emotional types of these 90 pieces of music. Mark the corresponding data table as music2. Like music1, music2 also has 5 attribute fields: title, happy, animation, sad, and Calm. If a piece of music belongs to a certain emotional type, the corresponding emotional value is recorded as “1”; otherwise, it is recorded as “0”.
It is necessary to connect to the database when making the search page. The key is the setting of the retrieval language. The traditional retrieval method
Finally, collect and organize data. First, retrieve all music tracks with the emotion “happy” in the data table music1 and record the total number of retrieved records N1. Record the other 3 emotions in the same way. The data sheet music2 is similarly processed and marked as N2. The statistical results of the number of retrieved records are listed in Table 1.
Search results of music1 and music2.
|Number of records||106||90|
In addition, the retrieval ability and retrieval accuracy of this retrieval method is examined from the three aspects of recall, precision, and F value. Recall rate refers to the percentage of the detected related documents to all related documents in the exhaustive literature. We denote it as R. The precision rate refers to the percentage of the detected related documents to the total number of detected documents . We will denote it as P. For example, the emotional type of “happy” in Table 1 is R=89.2%. “33” means the number of recorded music resources related to happiness; “37” means all of the music resources with a membership degree of “happy” greater than zero that can make people feel the emotion “happy” a little bit. The total number of records of music resources. Figure 1 and Figure 2 show the recall and precision of 4 different emotions obtained by the above two retrieval methods.
In general, the number of all relevant documents for a certain question cannot be accurately known, so the calculation results of R are generally approximate. However, if the specific information and sample number of all tested resources have been clarified in advance, the R-value of each emotion type can be obtained here . Recall rate and precision rate indicate certain aspects of retrieval methods. But which retrieval method is better requires further integration of recall and precision. Therefore, the harmonic average F of P and R is introduced to examine the retrieval ability. Where
Finally, the performance of these two retrieval methods is evaluated from a user-oriented perspective. Mainly examine the coverage rate C and the new rate N. Where C=Ru/U, U represents the total number of related documents known by the user. Ru represents the resulting number of the intersection of search results, and U. C represents the proportion of related documents known to the user found by the system . In random interviews with 10 users, we asked them to choose the music that belongs to each emotion among the 90 music according to their ideas. For the emotion of “happy,” we need to ask 10 users to tell the music collection they think belongs to this emotion and mark them as
The number of music records of the same emotion type in music1 in Table 1 is more than the number of music records in music2. The total number of retrieved items is 16 more than music 2. This shows that traditional methods of emotion retrieval ignore the diversity of music emotions, and fuzzy retrieval can better reflect the diversity of music emotions.
The fuzzy search results are arranged in descending order according to the degree of membership from high to low. If the user needs to find all the music with the emotional “happy,” then the final result presented to the user is that the music with the membership degree of 1 is ranked first, the more happy music is ranked second, and the last one is the membership degree of 0.3. If it is a traditional retrieval mode, then the results retrieved by the user will not have this rule. It is only arranged according to filling in the datasheet music2.
From Figure 1, the retrieval capability of fuzzy retrieval is beyond doubt. The recall rate is close to 90%. The recall rate depends on the precision rate. The increase in precision is conducive to the increase in recall. Therefore, in general, the retrieval ability of this retrieval method is relatively excellent.
It can be seen from Figure 2 that the average recall rate of traditional retrieval methods is only 72.3%. Compared with a fuzzy query, its recall rate is lower than 16%. And the emotion of anxiety and sadness have a certain similarity, so the probability of misjudgment is higher when non-quantitatively judging which type of emotion a certain piece of music belongs to. At the same time, the precision and recall of these two types of emotions will also impact . Through comparison, it is found that the retrieval ability of fuzzy retrieval is much higher than that of ordinary non-quantitative queries.
It can be seen from Figure 3 that the F values of these two retrieval methods are both above 65%. For fuzzy retrieval, the F value of each emotion is above 93%. This is higher than the F value of the traditional retrieval method. Especially the emotion of “anxiety” and the emotion of “sad” have great similarities.
It can be seen from Figure 4 that fuzzy retrieval can cover more relevant resources that users already know. As “happy” and “sad” are two emotional music resources. Their coverage rate has reached 100%. The coverage rate of fuzzy retrieval of the intermediate type of emotion “anxiety” is much higher than traditional retrieval . This also shows that fuzzy retrieval can better reflect the diversity of music emotions.
The N-value curve of fuzzy retrieval in Fig. 5 is located above the N-value curve of traditional retrieval. This means that the new rate of fuzzy retrieval is higher than that of traditional retrieval. From the user’s point of view, the fuzzy search can more include the user’s search needs on the one hand and can also provide users with some new and more relevant music resources outside of cognition.
The fuzzy retrieval method of music technically satisfies the diversity of music emotions. This makes the digital work of music closer to the essential characteristics of music. The experimental results prove that it is feasible and effective to realize the retrieval of music emotion based on fuzzy mathematics.
Search results of music1 and music2.
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Educational Model of College Physical Education Course Education Professional English Translation Corpus Under the Binomial Theorem Coefficient Geometric Tolerance Control Method for Precision Machinery Based on Image Modeling and Novel Saturation Function Retrieval and Characteristic Analysis of Multimedia Tester Based on Bragg Equation Semiparametric Spatial Econometric Analysis of Household Consumption Based on Ordinary Linear Regression Model Video adaptive watermark embedding and detection algorithm based on phase function equation English Learning Motivation of College Students Based on probability Distribution Scientific Model of Vocational Education Teaching Method in Differential Nonlinearity Research on mobile Awareness service and data privacy Protection based on Linear Equations computing protocol Vocal Music Teaching Model Based on Finite Element Differential Mathematical Equations Research on threat assessment problems of island air defence system based on the leader-follower model Studying a matching method combining distance proximity and buffer constraints The trend and influence of media information Propagation based on nonlinear Differential equation Research on the construction of early warning model of customer churn on e-commerce platform Study on inefficient land use determination method for cities and towns from a city examination perspective A sentiment analysis method based on bidirectional long short-term memory networks Evaluation of ecosystem health in Futian mangrove wetland based on the PSR-AHP model A study of local smoothness-informed convolutional neural network models for image inpainting Towards more efficient control of the ironmaking blast furnace: modelling gaseous reduction of iron ores in H2-N2 atmosphere Algorithm of overfitting avoidance in CNN based on maximum pooled and weight decay Mathematical Calculus Modeling in Improving the Teaching Performance of Shot Put Application of Nonlinear Differential Equation in Electric Automation Control System Higher Mathematics Teaching Curriculum Model Based on Lagrangian Mathematical Model Decisions of competing supply chain with altruistic retailer under risk aversion Optimization of Color Matching Technology in Cultural Industry by Fractional Differential Equations The Marketing of Cross-border E-commerce Enterprises in Foreign Trade Based on the Statistics of Mathematical Probability Theory Application of Linear Partial Differential Equation Theory in Guiding Football Scientific Training The Evolution Model of Regional Tourism Economic Development Difference Based on Spatial Variation Function System Model of Shipping Enterprise Safety Culture Based on Dynamic Calculation Matrix Model The Inner Relationship between Students' Psychological Factors and Physical Exercise Based on Structural Equation Model (SEM) Analysis and Research on Influencing Factors of Ideological and Political Education Teaching Effectiveness Based on Linear Equation Fractional Differential Equations in Sports Training in Universities Examination and Countermeasures of Network Education in Colleges and Universities Based on Ordinary Differential Equation Model Higher Education Agglomeration Promoting Innovation and Entrepreneurship Based on Spatial Dubin Model Chinese-English Contrastive Translation System Based on Lagrangian Search Mathematical Algorithm Model