28. The method according to claim 1 , wherein the one or more attributes are at least one of (i) on a continuous scale and (ii) a binary classification type.
1. A method of predicting a content attribute, comprising the steps of: collecting, via an input interface, one or more labeled media objects including audio content, wherein the one or more labeled media objects are labeled with one or more training attributes, the training attributes including at least one of danceability, energy, speechiness, liveness, acousticness, valence, and instrumentalness; storing, in a ground-truth database, the one or more training attributes; extracting, by a local feature extraction engine, one or more local features associated with the audio content; retrieving from the one or more labeled media objects, by a mid-level feature extraction engine, one or more mid-level features associated with the audio content; constructing, by a selector engine, a feature vector of one or more content-derived values for each of the one or more labeled media objects based on (i) the one or more local features, (ii) the one or more mid-level features, or (iii) a combination of (i) and (ii); generating, using a machine learning processor, a model based on the feature vector and the one or more attributes; receiving, via the interface, one or more unlabeled media objects including audio content; and applying the model to the one or more unlabeled media objects to generate one or more predicted acoustic attribute labels corresponding to the audio content of the unlabeled media objects, the predicted acoustic attribute labels including at least one of danceability, energy, speechiness, liveness, acousticness, valence, and instrumentalness; and providing the one or more predicted acoustic attribute labels to a processor device to generate and output to a peripheral device at least one of (i) a playlist, (ii) a recommendation, (iii) a catalog search result, and (iv) one or more catalogs categorized into one of a plurality of subclasses.