Proceedings ArticleDOI
Classification of audio codecs based on coding characteristics
Samet Hicsonmez,Husrev T. Sencar,Ismail Avcibas +2 more
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TLDR
A fast, simple and new method for identification of audio codecs that does not require decoding of coded audio data and assuming no knowledge on encoding structure of a codec is proposed.Abstract:
We propose a fast, simple and new method for identification of audio codecs that does not require decoding of coded audio data. The method uses chaotic and randomness features of coded audio to build models associated with different codecs. The most important features of the method are operating on just a few kilobytes of data sampled randomly from audio file and assuming no knowledge on encoding structure of a codec. These two features make it fast and simple. Experiments are performed on both singly coded and transcoded audio samples to measure accuracy of the technique.read more
References
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Book
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Marina Bosi,Richard E. Goldberg +1 more
TL;DR: This paper presents a meta-modelling framework for building a Perceptual Audio Decoder that automates the very labor-intensive and therefore time-heavy, and therefore expensive, and expensive, process of Audio Coding.
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TL;DR: In this article, the most prominent speech coding standards are presented and their properties, such as performance, complexity, and coding delay, analyzed, and specific networks and applications for each standard are included.
Proceedings ArticleDOI
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Rui Yang,Yun Q. Shi,Jiwu Huang +2 more
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Proceedings ArticleDOI
PinDr0p: using single-ended audio features to determine call provenance
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