  The shuffle mode, where songs are played in a randomized order that is
decided upon for all tracks at once, is widely found and known to exist in
music player systems. There are only few music enthusiasts who use this mode
since it either is too random to suit their mood or it keeps on repeating the
same list every time. In this paper, we propose to build a convolutional deep
belief network(CDBN) that is trained to perform genre recognition based on
audio features retrieved from the records of the Million Song Dataset. The
learned parameters shall be used to initialize a multi-layer perceptron which
takes extracted features of user's playlist as input alongside the metadata to
classify to various categories. These categories will be shuffled
retrospectively based on the metadata to autonomously provide with a list that
is efficacious in playing songs that are desired by humans in normal
conditions.
