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Non-intrusive Identification of Speech Codecs in Digital Audio Signals false
By:Frank Jenner
Published on 2011 by

|The use of speech codecs plays a very important role in modern telecommunications networks. Decades of extensive research have yielded voice compression techniques and improvements that have given rise to numerous speech codec standards. However, despite an interesting array of potential applications, very little research has been performed with regard to the ability to distinguish between these codecs in an audio signal. The identification of codecs in speech signals could be used to provide information about call origins, to aid in network diagnostics related to call quality, or to decide how to better enhance the signal prior to performing speech recognition. The research presented in this paper seeks to provide a novel approach for accurately identifying among several common speech codecs from a speech signal. The developed approach is non-intrusive, requiring no information about the original input signal, nor access to the compressed bitstream. Instead, the identification is performed by analyzing only the reconstructed audio signal. This is a particularly challenging task because all codecs strive to output a signal that is perceptually indistinguishable from the original. As a result, there are only very subtle artifactual differences between speech signals processed with different codecs. The identification technique developed in this research involves analyzing the input signal to generate a profile that characterizes several features of the input signal. The features include several noise spectra that attempt to isolate artifactual noise components in the signal from the signal components that fit a particular speech model, as well as a histogram of sample amplitudes that attempts to capture quantization patterns in the signal. The profiling procedure is first applied to signals that have been processed with known codecs in order to create a set of training profiles. To then identify the codec in an unknown test signal, the same profiling procedure is applied, and the resulting profile is compared to each of the training profiles to decide which codec is the best match. Overall, the proposed strategy generates extremely favorable results, with an average of 90% of all test signals' codecs being correctly identified. In addition, the profiling process is shown to require a very small analysis window of less than 4 seconds of signal to achieve these results. Both the identification rate and the small analysis window represent dramatic improvements over previous efforts in speech codec identification.|--Abstract.

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