利用貝氏方法透過聲音指紋辨識場所

鄭翔之

doi:10.6842/NCTU.2014.00303

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利用貝氏方法透過聲音指紋辨識場所

Location Recognition via Audio Fingerprint by the Generalized Bayes Method

鄭翔之 , Masters　　Advisor：王秀瑛;曾煜棋　　

英文 DOI： 10.6842/NCTU.2014.00303

聲音背景頻譜； MFCC ；混合高斯分佈；最大期望演算法；貝氏方法； Acoustic background spectrum ； MFCC ； GMM ； EM-algorithm ； Generalized Bayes method

Abstract │ Reference (19) │ Altmetrics

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Location Recognition has attracted a lot of researchers' attention in the past few years. Recently, using acoustic background spectrum for indoor localization through mobile device, such as smartphone, had been discussed in the literature. The advantage of location recognition through audio is because it is compact, easy computed, robust to transient sounds, and distinctive. In general, acoustic spectrum analysis is based on Mel-frequency cepstral coefficients (MFCCs) to transform audio data to high dimensional data, and these high dimensional data can be fitted by Gaussian mixture models. Based on the Gaussian mixture models fitted by the training data, we can classify the audio fingerprint of a location to a Gaussian mixture model with the highest likelihood value.
To estimate the parameters in the Gaussian mixture models, it is common to use the expectation-maximization algorithm (EM-algorithm) to estimate the parameter. In this thesis, we apply the generalized Bayes method to estimate the parameter in GMMs, and compare it with the EM-algorithm. In a simulation study and a real data example, the generalized Bayes method is shown to have better performance than the EM algorithm.

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