  In this paper we present a research on identification of audio recording
devices from background noise, thus providing a method for forensics. The audio
signal is the sum of speech signal and noise signal. Usually, people pay more
attention to speech signal, because it carries the information to deliver. So a
great amount of researches have been dedicated to getting higher
Signal-Noise-Ratio (SNR). There are many speech enhancement algorithms to
improve the quality of the speech, which can be seen as reducing the noise.
However, noises can be regarded as the intrinsic fingerprint traces of an audio
recording device. These digital traces can be characterized and identified by
new machine learning techniques. Therefore, in our research, we use the noise
as the intrinsic features. As for the identification, multiple classifiers of
deep learning methods are used and compared. The identification result shows
that the method of getting feature vector from the noise of each device and
identifying them with deep learning techniques is viable, and well-preformed.
