![]() However, too many emotional tags will make the mood classification complex. For example, Gracenote has developed its mood taxonomy of more than 400 emotional qualities (Roettgers & Roettgers, 2017). When using music apps, users could find a variety of mood tags on the home page. With the advancement of AI, the algorithms iterate over and over again, and now there are various algorithms to achieve the goal of music mood classification. Researchers will extract features from music, and then put those features into a classifier to identify different kinds of music mood. Briefly speaking, researchers will firstly set a dataset to train the machines, helping them detect different kinds of music mood. Therefore, the researchers turn to the power of AI for help to do this work. Besides, it seems to be inevitable for people to make mistakes when classifying music personally. Manual classification is time-consuming work. However, a challenging problem that arises in this domain is that classifying hundreds of millions of songs in the music library manually is not practical at all. Users of Rok music service could use a mood grid to play songs based on their feelings (Hayes et al., 2014). Spotify acquired Echo Nest to define audiences by moods and activities using the music mood data ( A “Hit” for Every Mood - Spotify’s Analysis of Our Emotional States, 2019). For example, Gracenote started its sonic emotion classification about 10 years ago (Roettgers & Roettgers, 2017). Among all these classification methods, music mood classification is one of the most popular methods, and many music companies have begun to work on this. With the increasingly urgent need for high-quality music classification, many content-based music classification methods have been proposed, which extract features from the music itself, such as the melody, the performance style, etc. It ignores users’ direct feelings about the music, therefore, in the most time, this kind of classification cannot provide users with the most accurate recommendation of what they want. Such a method is convenient, but the disadvantage is also obvious. Currently, the common classification method is mostly based on the external characteristics of music, such as singer, band, year, album, and so on. In this case, the music classification system begins to play an important role in music discovery for users. As the music consumption paradigm moves towards online streaming services, users have access to increasingly large online music library. With the booming development of network technology, there are more and more online streaming services. ![]() Music mood classification becomes one of the best evidence of the music industry’s increasing reliance on artificial intelligence. This article aims to explore how music mood classification system works technically, including the taxonomy and identification of the music mood, as well as the mechanism of machine learning on music mood classification system. Mood classification system improves users’ experience with online music streaming services besides, advertisers could analyze the relationship between music, users’ mood, and their behaviors to re-understand their target consumers emotionally. Music mood classification is now becoming more and more important for most music streaming services. Explore the Music World: Categorize Music by Mood
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