Music streaming services such as Spotify have revolutionized the way we consume music. With the ability to access millions of songs from all over the world, it has become easier than ever to discover new music. However, one of the challenges that comes with this is the difficulty of accurately classifying music into its respective genres. This is especially true for multiclass genre classification, which involves assigning a song to multiple genres.
In order to address this issue, researchers have explored a variety of solutions. One approach is to use machine learning algorithms to classify songs into their respective genres. This involves training a model on a dataset of labeled songs, and then using the model to predict the genre of new songs. This approach has been shown to be effective in classifying songs into their respective genres.
Another approach is to use audio features such as tempo, rhythm, and timbre to classify songs into their respective genres. This involves extracting features from a song and then using them to classify the song into its respective genre. This approach has been shown to be effective in classifying songs into their respective genres.
Finally, researchers have also explored the use of natural language processing (NLP) techniques to classify songs into their respective genres. This involves analyzing the lyrics of a song and using them to classify the song into its respective genre. This approach has been shown to be effective in classifying songs into their respective genres.
Overall, there are a variety of solutions that have been explored for multiclass genre classification on Spotify. By using machine learning algorithms, audio features, and natural language processing techniques, researchers have been able to develop effective models for accurately classifying songs into their respective genres. As such, these solutions can help make it easier for users to discover new music and explore different genres.
Source: Plato Data Intelligence: PlatoAiStream