M. K. Mihcak and R. Venkatesan, "A perceptual audio hashing algorithm: a tool for robust audio identification and information hiding," in Proc. Int. Information Hiding Workshop, Pittsburgh, PA, 2001.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....signature into the audio data using watermarking (see Figure 3) An example of such a system is described in [7] 4.3. Watermarking support Audio fingerprinting can assist watermarking. Audio fingerprints can be used to derive secret keys from the actual content. As described by Mihcak el al. [8], using the same secret key for a number of different audio items may compromise security, since each item may leak partial information about the key. Perceptual hashing can help generate input dependent keys for each piece of audio. Haitsma et al. 9] suggest audio fingerprinting to enhance the ....

.... Fingerprint Figure 2: Integrity verification framework be useful against insertion deletion attacks that cause desynchronization of the watermark detection: by using the fingerprint, the detector is able to find anchor points in the audio stream and thus to resynchronize at these locations [8]. 4.4. Content based audio retrieval and processing Deriving compact signatures from complex multimedia objects is an essential step in Multimedia Information Retrieval. Fingerprinting can extract information from the audio signal at different abstraction levels, from low level descriptors to ....

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M.K. Mihcak and R. Venkatesan, "A Perceptual Audio Hashing Algorithm: a Tool for Robust Audio Identification and Information Hiding," Proceedings of the 4th Workshop on Information Hiding, Pittsburg, PA, Ap. 2001.

.... (to avoid the curse of dimensionality ) and perform the indexing and searching in the reduced space [1] 3] In analogy to the cryptographic hash value, content based digital signatures can be seen as evolved versions of hash values that are robust to content preserving transformations [4] [5]. Also from a pattern matching point of view, the idea of extracting the essence of a class of objects retaining the main its characteristics is at the heart of any classification system [6] 10] II. GENERAL FRAMEWORK In spite of the different rationales behind the identification task, methods ....

.... Discrete Cosine Transform (DCT) the Haar Transform or the Walsh Hadamard Transform [2] Richly et al. did a comparison of the DFT and the Walsh Hadamard Transform that revealed that the DFT is generally less sensitive to shifting [13] The Modulated Complex Transform (MCLT) used by Mihcak et al. [5] and also by Burges et al. 14] exhibits approximate shift invariance properties [5] D. Feature Extraction Once on a time frequency representation, additional transformations are applied in order to generate the final acoustic vectors. In this step, we find a great diversity of algorithms. The ....

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M. Mihak and R. Venkatesan, "A perceptual audio hashing algorithm: a tool for robust audio identification and information hiding," in 4th Workshop on Information Hiding, 2001.

....a correct identification. The proposed system falls into the category of robust fingerprinting technologies. It is designed to be identifying audio titles even if a fragment that has undergone distortions is used as query. The distortions robust systems aim to answer are enumerated for instance in [12], 13] 6] and [14] What we present in the next section is a description of the manipulations radio stations perform [15] BROADCAST RADIO PROCESSING Radio stations use complex sound processing to get more loudness and a more impressive sound. The major intention of these stations is to attract ....

....to time and the y axis corresponds to semitones. A leaking of energy into the upper semitones is appreciated in the broadcast version. Although this effect is less used nowadays, the distortion can affect the performance of some fingerprinting systems depending on the features and modeling used[12]. Sound Processors Professional radio stations use complex sound processors which comprise all of the effects devices discussed above (e.g. Omnia or Optimod) Sometimes, the results are better if the signal is compressed a little bit (2:1) before going through the sound processor, so other ....

M. K. Mihak and R. Venkatesan, "A Perceptual Audio Hashing Algorithm: A Tool For Robust Audio Identification and

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M. K. Mihcak and R. Venkatesan, "A perceptual audio hashing algorithm: a tool for robust audio identification and information hiding," in Proc. Int. Information Hiding Workshop, Pittsburgh, PA, 2001.

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