Abstract: Systems, methods, and non-transitory computer-readable media can receive training data for training a machine learning model. The machine learning model is trained based on the training data. The machine learning model is trained to identify highlighted portions in audio content items. A first audio content item is received. One or more highlighted portions are identified in the first audio content item based on the machine learning model.

Abstract: Systems and methods for feature-based music selection may include (1) receiving user input selecting a music composition, (2) identifying features of the music composition including (i) a musical feature, relating to a musical quality of the music composition and (ii) a lyrical feature, relating to one or more of the music composition's lyrics, (3) determining that an additional music composition is similar to the music composition based on a comparison of the features of the music composition with features of the additional music composition, and (4) selecting the additional music composition to be added to a queue associated with the music composition based on the determination. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Systems, methods, and non-transitory computer-readable media can be configured to determine a video embedding for a video content item based at least in part on a first machine learning model. A set of music embeddings can be determined for a set of music content items based at least in part on a second machine learning model. The set of music content items can be ranked based at least in part on the video embedding and the set of music embeddings.

Abstract: Systems, methods, and non-transitory computer-readable media can be configured to determine popularity scores for a set of audio content items. The set of audio content items can be ranked based at least in part on the popularity scores, the popularity scores based at least in part on signals associated with a social networking system. A music chart can be generated based at least in part on the ranking.

Abstract: Systems, methods, and non-transitory computer-readable media can be configured to determine popularity scores for a set of audio content items. The set of audio content items can be ranked based at least in part on the popularity scores, the popularity scores based at least in part on signals associated with a social networking system. A music chart can be generated based at least in part on the ranking.

Abstract: Systems, methods, and non-transitory computer-readable media can be configured to identify audio content included in a video content item. A notification can be provided based at least in part on the identification. An interface that presents information associated with the identified audio content can be provided based at least in part on a user interaction with the notification.

Abstract: Systems, methods, and non-transitory computer-readable media can be configured to determine a video embedding for a video content item based at least in part on a first machine learning model. A set of music embeddings can be determined for a set of music content items based at least in part on a second machine learning model. The set of music content items can be ranked based at least in part on the video embedding and the set of music embeddings.

Abstract: Systems and methods for text-based music selection through a music service may include (1) determining that a user of a social media application may be interested in sharing, via the social media application, music that relates to one or more key words, (2) providing the user with a music recommendation that relates to the one or more key words, (3) receiving input from the user accepting the music recommendation, and (4) in response to receiving the input, sharing the music composition via the social media application. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Systems, methods, and non-transitory computer-readable media can be configured to determine a video embedding for a video content item based at least in part on a first machine learning model. A set of music embeddings can be determined for a set of music content items based at least in part on a second machine learning model. The set of music content items can be ranked based at least in part on the video embedding and the set of music embeddings.

Abstract: Systems, methods, and non-transitory computer-readable media can be configured to identify audio content included in a video content item. A notification can be provided based at least in part on the identification. An interface that presents information associated with the identified audio content can be provided based at least in part on a user interaction with the notification.

Abstract: Systems, methods, and non-transitory computer-readable media can be configured to determine popularity scores for a set of audio content items. The set of audio content items can be ranked based at least in part on the popularity scores, the popularity scores based at least in part on signals associated with a social networking system. A music chart can be generated based at least in part on the ranking.

Abstract: Systems, methods, and non-transitory computer-readable media can receive a user selection of an audio content item. Highlighted portion information associated with the audio content item is received. The highlighted portion information identifies one or more highlighted portions in the audio content item. A user interface is provided based on the highlighted portion information. The user interface comprises one or more highlight indicators. Each highlight indicator is associated with a respective highlighted portion of the one or more highlighted portions. Each highlight indicator is selectable by a user to select the associated highlighted portion.