  We propose the Neuralogram -- a deep neural network based representation for
understanding audio signals which, as the name suggests, transforms an audio
signal to a dense, compact representation based upon embeddings learned via a
neural architecture. Through a series of probing signals, we show how our
representation can encapsulate pitch, timbre and rhythm-based information, and
other attributes. This representation suggests a method for revealing
meaningful relationships in arbitrarily long audio signals that are not readily
represented by existing algorithms. This has the potential for numerous
applications in audio understanding, music recommendation, meta-data extraction
to name a few.
