TWI482149B - The Method of Emotional Classification of Game Music - Google Patents

Description

Emotional classification method of game music

The present invention relates to a music classification method, and more particularly to a method for classifying emotions of game music.

In the era of information explosion, the acquisition of music is quite easy. With the growing popularity of video games, many video games will later release game soundtracks for the game music they use, or even hold concerts. In order to match the content of the game scene, the game music is usually story-like. Therefore, if the game music can be classified by emotion and the player can select the game music that suits his mood, the game game will be more affected by the player. favorite.

Although there are currently developers who have developed so-called software systems or devices that can classify music emotions, the classification methods of such software systems or devices are based on pre-set rules of music to classify music emotions, because everyone feels about music. They are not the same, and their classification results are often not accepted by users. Therefore, this method of classification cannot be applied to everyone and its applicability is poor.

Accordingly, it is an object of the present invention to provide an emotional classification method for emotionally classifying game music through tonality, interval and rhythm.

Another object of the present invention is to provide an emotion classification method for a user to classify emotions of game music according to his or her own feelings.

Therefore, the emotional classification method of the game music of the present invention is applicable to being constructed in an electronic device through software and/or hardware, and is used for the game. Music classifying emotions includes the following steps: (A) respectively setting a corresponding emotional adjective parameter for the plurality of game music samples stored in the electronic device; (B) performing tonality on each of the game music samples of step (A) Music characteristics such as rhythm and interval, and outputting a plurality of music feature parameters for each game music sample; (C) statistically analyzing the emotional adjective parameters of the game music samples by the multi-category support vector machine respectively and corresponding thereto Correlation of music feature parameters, and constructing a multi-category emotion prediction model; and (D) performing music feature analysis on each new game music stored subsequent to the electronic device, and correspondingly outputting a plurality of music feature parameters, and The multi-category emotion prediction model analyzes the music feature parameters of the new game music, and emotionally classifies the music game music, and outputs a pair of emotional adjective parameters that should be new game music.

The effect of the invention: by using the support vector machine to analyze the musical characteristic parameters such as tonality, interval and rhythm of the game music, and the statistical analysis of the emotional adjective parameters set by the game music, Game music is a multi-category emotion prediction model that classifies emotions and has high classification accuracy.

The above and other technical contents, features and advantages of the present invention will be apparent from the following detailed description of the preferred embodiments.

As shown in FIG. 1 , a preferred embodiment of the method for classifying emotions of the game music of the present invention is applicable to software and/or hardware built in an electronic device, and the game music stored in the electronic device can be used. Emotion classification. The emotion classification method comprises the following steps: Step (1) setting an emotional adjective parameter to a game music sample. First, a plurality of emotional adjective parameters representing the emotional attribution of the game music samples whose emotion classification has been determined are respectively set.

In this embodiment, the game music samples are divided into five types of emotions, and each of the emotion categories corresponds to an emotional adjective and an emotional adjective parameter, and the emotional adjectives are “quiet” and “cold” respectively. "Singular", "humorous", and "enthusiasm". However, the type of emotional adjectives used is not limited to the above types.

Step (2) Performing music feature analysis on all game music samples to obtain music feature parameters of the game music samples. Each of the game music samples is analyzed for three musical characteristics such as tonality, interval and rhythm, and the music characteristic parameters of each game music sample are output correspondingly.

Among them, the analysis of tonal music characteristics is to analyze the tonal types contained in each game music sample, and the proportion of various tonality (%), such as C major, D major, E major and B Major adjustments, and the proportion of tonality in C minor, D minor and B minor, and statistical analysis of the average proportion (%) of various tonality of all game music samples corresponding to each emotional adjective parameter. The interval music feature is to analyze the complete consonant interval, incomplete consonant interval, and the proportion of non-coordinated intervals used in each game music sample, and statistically analyze the complete concord of all game music samples corresponding to each emotional adjective parameter. The interval (%) of the interval, the incomplete harmony interval, and the average proportion of the uncoordinated intervals. The analysis of the rhythm music feature is to analyze the beat per minute (bpm) of each game music sample, and statistical scores. The average rhythm velocity of all game music samples corresponding to each emotional adjective parameter is precipitated.

After analyzing the music features of each game music sample, the music features are data encoded and normalized to produce desired music feature parameters.

In this embodiment, the tonal music features, the interval music features, and the rhythm music features of the game music samples are separately analyzed by the MATLAB software, but when implemented, due to the numerous analysis methods of the aforementioned music features, the case is not The software used is limited. In addition, since data coding and normalization are common technical means for existing statistical analysis, and there are many ways, they will not be described in detail.

Step (3) establish a multi-category emotion prediction model with Support Vector Machine (SVM). The multi-category emotion prediction model is established by the support vector machine for the emotional adjective parameters set in step (1) for each game music sample, and (2) the music feature parameters obtained from the analysis of the game music samples.

In this embodiment, statistical analysis is performed according to the music feature parameters of each game music sample and the emotional adjective parameters corresponding to each game music sample, thereby forming a multi-category classification problem, and then decomposing the multi-category classification problem into one A one-to-one (One-against-one, OAO) support vector machine model, cross-validated by Gaussian kernel function, and based on the judgment results of all support vector machine models, a multi-category emotion is established. Forecast model. At this time, the multi-category emotion prediction model can be used to emotions other game music stored separately for the electronic device. classification.

Step (4) analyzes the importance of the musical feature parameters for each emotional adjective parameter. According to the multi-category emotion prediction model established in step (3), the support vector machine recursive feature elimination (SVMREF) is further used to analyze the weight of each music feature parameter on each emotional adjective parameter. And outputting the weighting order data of the music feature parameters in each of the emotional adjective parameters, as shown in Table 1.

This weight sorting order can facilitate game music producers to understand which music features are more influential for the expression of which emotions, and help to make the emotions expressed by the produced game music more in line with the game story, and more likely to trigger the game. The emotional resonance of the person.

Step (5) emotionally classifying the other game music stored in the electronic device by the multi-category emotion prediction model. After the multi-category emotion prediction model is established according to the foregoing game music sample, the multi-category emotion prediction model can perform the emotion classification processing of the game music. When classifying, the music features of the new game music with the classification are also analyzed first. The music feature parameters such as tonality, interval and rhythm of each new game music to be classified are analyzed, and then the music feature parameters of each new game music to be classified are analyzed by the multi-category emotion prediction model, and Equivalent adjective parameters corresponding to each new game music to be classified, and then analyzing the emotion categories to which each new game music belongs, and after completing the emotion analysis, driving the electronic device to feedback to display a corresponding to the analyzed An animated image of the emotional adjective parameters of the new game music to alert the user of the electronic device.

Step (6) classifying and storing the new game music after analysis according to the parameters of the emotional adjectives, and establishing a game music emotion classification database.

Through the above steps, the present invention analyzes the musical feature parameters such as tonality, interval and rhythm of the game music samples, and performs statistical analysis of the support vector machine with five kinds of emotional adjective parameters to construct a multi-category emotion prediction model. The emotion prediction model has high emotional classification accuracy, and it can effectively perform accurate emotion classification according to the music characteristic parameters of the game music.

It should be noted that the preliminary emotional classification of the aforementioned game music sample may be a classification made by the user who constructs the electronic device with the method according to his own listening feeling, so the multi-category emotion prediction model established in step (3) The game music emotion classification mode will be close to the personal preference of the user. Therefore, the emotion classification method of the game music of the present invention can conveniently construct the user of the electronic device of the method, and establish an exclusive multi-category emotion according to the feeling of the user. The prediction model makes the classification result of the game music more in line with the needs of the user.

Of course, during the implementation, the emotional classification of the game music samples can also be generated through the analysis of the questionnaire. At this time, the game classification results of the multi-category emotion prediction model constructed by the method will be closer to the general public. Sexual cognition, which can be applied to game software developers.

Therefore, the emotion classification method of the game music of the present invention can conveniently establish an exclusive multi-category emotion prediction model that meets various needs according to the emotional classification difference of the game music initially established by the user, and can improve the traditional music emotion classification system only according to the basis. The previously set system analysis mode has the disadvantage of classifying music emotions.

In summary, by using the support vector machine to analyze the musical characteristic parameters such as tonality, interval and rhythm of the game music, and the statistical analysis of the emotional adjective parameters set by the game music, it is possible to construct a Game music is a multi-category emotion prediction model with emotional classification and high classification accuracy, which will help video game players and video game developers to quickly obtain the emotional classification of game music and understand the influence of various music feature parameters on emotional attributes. Force, game music developers can design game products that meet the needs of players for specific game emotional content or situations. In addition, the present invention can also be used by the user to establish an exclusive multi-category emotion prediction model according to the emotional classification of the game music samples made by the user according to his own listening experience, so that the emotional classification result of the game music can be further improved in the future. Close to their own preferences, but can improve the traditional music sentiment classification system can only be based on its original set of analysis mode for music emotional classification of the shortcomings, quite convenient and practical. Therefore, the object of the present invention can be achieved.

However, the above is only the preferred embodiment of the present invention, when not The scope of the invention is to be construed as being limited by the scope of the invention and the scope of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 is a flow chart showing the steps of a preferred embodiment of the emotion classification method for game music of the present invention.

Claims (3)

A method for classifying emotions of game music, which is suitable for constructing an electronic device through software and/or hardware, and for classifying emotions of the game music, comprising the following steps: (A) storing the plurality of pieces of the electronic device The game music sample respectively sets a corresponding emotional adjective parameter; (B) performs music feature analysis such as tonality, rhythm and interval for each game music sample of step (A), and outputs multiple music samples corresponding to each game. Music feature parameters; (C) statistically analyzing the relationship between the emotional adjective parameters of the game music samples and the corresponding music feature parameters by multi-class support vector machine, and constructing a multi-category emotion prediction model; (F) According to the multi-category emotion prediction model of step (C), the support vector machine recursive feature elimination method is used to analyze the order weight order of each music feature parameter for each emotional adjective parameter, and the music feature parameters are output in each emotional adjective. The weighting order data in the parameters; and (D) the music of each new game music stored in the subsequent storage of the electronic device Corresponding to the analysis, correspondingly outputting a plurality of music feature parameters, and analyzing the music feature parameters of the new game music by the multi-category emotion prediction model, and classifying the emotions of the game music, and outputting a pair of new game music Emotional adjective parameters. According to the emotional classification method of the game music according to claim 1, the method further comprises a step (E): analyzing the new one according to the step (D) The emotional adjective parameter corresponding to the game music stores the new game music classification, and establishes a game music emotion classification database. According to the emotion classification method of the game music described in claim 1, the step (D) also displays an image via the electronic device for reminding when analyzing the emotional adjective parameter of each new game music.