Abstract: A neural network-based classifier system can receive a query including a media signal and, in response, provide an indication that the query corresponds to a specified media type or media class. The neural network-based classifier system can select and apply various models to facilitate media classification. In an example embodiment, a query can be analyzed for various characteristics, such as a noise profile, before it is input to the network-based classifier. If the query has greater than a specified threshold noise characteristic, then a successful classification can be unlikely and a classification process based on the query can be terminated before computational resources are expended. Query signals that meet or exceed a threshold condition can be provided to the network-based classifier for media classification. In an example embodiment, a remote device or a central media classifier circuit can determine a noise profile for a query.

Abstract: A user of a network-based system may correspond to a user profile that describes the user. The user profile may describe the user using one or more descriptors of items that correspond to the user (e.g., items owned by the user, items liked by the user, or items rated by the user). In some situations, such a user profile may be characterized as a “taste profile” that describes an array or distribution of one or more tastes, preferences, or habits of the user. Accordingly, the user profile machine within the network-based system may generate the user profile by accessing descriptors of items that correspond to the user, clustering one or more of the descriptors, and generating the user profile based on one or more clusters of the descriptors.