1. A method of identifying a room comprising the steps of: storing a database of a plurality of acoustic models for a plurality of identified rooms; inputting audio data from at least one microphone in an unknown room; extracting Mel-Frequency Cepstral Coefficient audio features from the input audio data; using machine learning to create a set of a plurality of parameters of functions as an acoustic model of the unknown room based upon the extracted Mel-Frequency Cepstral Coefficient audio features; comparing the acoustic model of the unknown room to the plurality of acoustic models in the database to determine a likelihood score that the model of the unknown room compares to respective ones of the acoustic models; determining the highest likelihood score of the unknown room to at least one of the acoustic models in the database; identifying the acoustic model of the unknown room as similar to the room in the database based on the likelihood score of the unknown room; and outputting the identity of the unknown room.