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URN etd-0831106-123747 Statistics This thesis had been viewed 3716 times. Download 1128 times. Author Teng-Kuei Chang Author's Email Address No Public. Department Computer Science and Enginerring Year 2005 Semester 2 Degree Master Type of Document Master's Thesis Language English Page Count 64 Title An Audio Feature Extraction Scheme Based on Secret Sharing and Wavelet Transform Keyword Torus automorphism feature extraction granularity secret sharing scheme discrete wavelet transform discrete wavelet transform secret sharing scheme granularity feature extraction Torus automorphism Abstract A novel audio feature extraction and identification scheme is proposed in this thesis. The proposed scheme uses the discrete wavelet transform (DWT) and the concept of secret sharing scheme to improve the robustness and reliability. Hence, the granularity, the minimal length of audio, needed for identification in an audio fingerprinting system, can be reduced without decreasing the efficiency of the system. The scheme employs binary share images to substitute the hash values and the fingerprints stored in the database. The suspect audio signal is then identified by the following steps: 1. Extract the features of the suspect audio. 2. Decode the features with the share images in the database 3. Compare the decoded image to an invariant logo. The experimental results prove the scheme is reliable and robust to some common audio processes. Additionally, the granularity can be reduced to 1.1 seconds, which is less than that of previous work. Advisor Committee Shang-Lin Hsieh - advisor
Files Date of Defense 2006-07-17 Date of Submission 2006-08-31