Abstract: A system and method are disclosed for providing mode-based recommendations to a user. In an embodiment, the system determines, during a media streaming session of a user, that the user has selected a seed for the media streaming session using a media streaming application installed at a client device of the user. The system determines a playlist for the media streaming session that includes a plurality of media objects based on the seed. The system determines characteristics for each media object in the playlist, and compares the characteristics to the minimum criteria of candidate modes. The system identifies a subset of modes from the candidate modes for which the minimum criteria is met by the characteristics of the media objects. The system instructs the media streaming application to provide a selectable option of a mode of the subset for display.

Abstract: A content server uses a form of artificial intelligence such as machine learning to identify audio content with musicological characteristics. The content server obtains an indication of a music item presented by a client device and obtains reference music features describing musicological characteristics of the music item. The content server identifies candidate audio content associated with candidate music features. The candidate music features are determined by analyzing acoustic features of the candidate audio content and mapping the acoustic features to music features according to a music feature model. Acoustic features quantify low-level properties of the candidate audio content. One of the candidate audio content items is selected according to comparisons between the candidate music features of the candidate audio advertisements and the reference music features of the music item. The selected audio content is provided the client device for presentation.

Abstract: A content server uses a form of artificial intelligence such as machine learning to identify audio content with musicological characteristics. The content server obtains an indication of a music item presented by a client device and obtains reference music features describing musicological characteristics of the music item. The content server identifies candidate audio content associated with candidate music features. The candidate music features are determined by analyzing acoustic features of the candidate audio content and mapping the acoustic features to music features according to a music feature model. Acoustic features quantify low-level properties of the candidate audio content. One of the candidate audio content items is selected according to comparisons between the candidate music features of the candidate audio advertisements and the reference music features of the music item. The selected audio content is provided the client device for presentation.