基於秘密分享及小波轉換之音訊特徵萃取

An Audio Feature Extraction Scheme Based on Secret Sharing and Wavelet Transform

張登貴(Teng-Kuei Chang)

指導教授 ： 謝尚琳

大同大學/電機資訊學院/資訊工程學系所/碩士(2006年)

摘要

本論文提出一種新的音訊特徵抽取及音訊辨識的機制。這個機制於小波轉換後的頻率係數上抽取特徵，並且利用秘密分享機制的概念來增強辨識的效能。因此，在不使辨識效能降低的條件下，可以縮短用來做音訊辨識的最短音訊長度。其中我們使用一張二元化的share影像來取代儲存在資料庫中的特徵。此系統是藉由以下三個步驟來辨識一個未知的音訊。1.萃取音訊特徵。2.將此特徵與share影像解碼。3.將解碼出來的結果與一張不變的logo做比對。實驗結果證明此機制是可信賴的並且能抵抗一般的音訊處理。此外，用來做音訊辨識的最短長度可縮短為1.1秒，低於前人之研究。

關鍵字

特徵萃取 ； 離散小波轉換 ； 秘密分享機制 ； 粒度

並列摘要

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.

並列關鍵字

discrete wavelet transform ； secret sharing scheme ； granularity ； feature extraction ； Torus automorphism

參考文獻

[1] L. Gomes, P. Cano, E. Gómez, M. Bonnet, and E. Batlle, "Audio Watermarking and Fingerprinting: For Which Applications?," Journal of New Music Research, Volume 32, Number 1, pp. 65–81, Mar. 2003.

[5] A. Ramalingam and S. Krishnan, "Gaussian Mixture Modeling Using Short Time Fourier Transform Features for Audio Fingerprinting," IEEE International Conference on Multimedia and Expo, ( ICME '05), pp. 1146 – 1149, Jul. 2005.

[6] J. Haitsma and T. Kalker, "A Highly Robust Audio Fingerprinting System," Proceeding of International Symposium on Musical Information Retrieval, (ISMIR '02), pp. 107-115, Oct. 2002.

[8] R. Lancini, F. Mapelli and R. Pezzano, "Audio Content Identification by using Perceptual Hashing," IEEE International Conference on Multimedia and Expo, ( ICME '04), Volume 1, pp. 27-30, Jun. 2004.

[9] S. L. Hsieh and H. C. Wang, "Feature Extraction for Audio Fingerprinting Using Wavelet Transformation," National Computer Symposium, (NCS '05), 2005.