14. A system for predicting a content attribute, comprising: an input interface configured to receive from an audio media object database 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; a ground-truth database configured to store the one or more training attributes; a local feature extraction engine configured to extract one or more local features associated with the audio content; a mid-level feature extraction engine configured to retrieve from the one or more labeled media objects one or more mid-level features associated with the audio content; a selector engine configured to construct 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); a machine learning processor configured to generate a model based on the feature vector and the one or more attributes; the input interface further configured to receive one or more unlabeled media objects including audio content; and an acoustic attribution prediction subsystem configured to apply the model to one or more unlabeled media objects to generate one or more predicted acoustic attribute labels, the predicted acoustic attribute labels including at least one of danceability, energy, speechiness, liveness, acousticness, valence, and instrumentalness; and a processor device configured to, based on the one or more predicted acoustic attribute labels, 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.
14. A system for predicting a content attribute, comprising: an input interface configured to receive from an audio media object database 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; a ground-truth database configured to store the one or more training attributes; a local feature extraction engine configured to extract one or more local features associated with the audio content; a mid-level feature extraction engine configured to retrieve from the one or more labeled media objects one or more mid-level features associated with the audio content; a selector engine configured to construct 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); a machine learning processor configured to generate a model based on the feature vector and the one or more attributes; the input interface further configured to receive one or more unlabeled media objects including audio content; and an acoustic attribution prediction subsystem configured to apply the model to one or more unlabeled media objects to generate one or more predicted acoustic attribute labels, the predicted acoustic attribute labels including at least one of danceability, energy, speechiness, liveness, acousticness, valence, and instrumentalness; and a processor device configured to, based on the one or more predicted acoustic attribute labels, 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. 21. The system according to claim 14 , wherein the audio waveform analyzer is further configured to: sample a segment of the one or more labeled media objects; identify one or more local content features in the segment; cluster the one or more local content features into one or more clusters; normalize and scale the one or more clusters according to a distribution in the one or more labeled media objects; and and concatenate the one or more clusters into the feature vector.