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In this letter, an acoustic environment classification algorithm based on the 3GPP2 selectable mode vocoder (SMV) is proposed for context-aware mobile phones. Classification of the acoustic environment is performed based on a Gaussian mixture model (GMM) using coding parameters of the SMV extracted directly from the encoding process of the acoustic input data in the mobile phone. Experimental results show that the proposed environment classification algorithm provides superior performance over a conventional method in various acoustic environments.
Akira IKUTA Mitsuo OHTA Noboru NAKASAKO
In the measurement of actual random phenomenon, the observed data often contain the fuzziness due to the existence of confidence limitation in measuring instruments, permissible error in experimental data, some practical simplification of evaluation procedure and a quantized error in digitized observation. In this study, by introducing the well-known fuzzy theory, a state estimation method based on the above fuzzy observations is theoretically proposed through an establishment of wide sense digital filter under the actual situation of existence of the background noise in close connection of the inverse problem. The validity and effectiveness of the proposed method are experimentally confirmed by applying it to the actual fuzzy data observed in an acoustic environment.
In this study, after focussing on an energy (or intensity) scaled variable of acoustic systems, first, a new regression analysis method is theoretically proposed by introducing a multiplicative noise model suitable to the positively scaled stocastic system. Then, the effectiveness of the proposed method is confirmed experimentally by applying it to the actual acoustic data.
In the actual acoustic environment, the stochastic process exhibits various non-Gaussian distribution forms, and there exist potentially various nonlinear correlations in addition to the linear correlation between time series. In this study, a nonlinear ARMA model is proposed, based on the Bayes' theorem, where no artificially pre-established regression function model is assumed between time series, while reflecting hierarchically all of those various correlation informations. The proposed method is applied to the actual data of road traffic noise and its practical usefulness is verified.
It often occurs in the acoustic environment that a specific signal is contaminated by the additional noise of non-Gaussian distribution type. In order to extract exactly the various statistical information of only specific signal from the observed noisy data, a stochastic signal processing by use of digital computer is essential. In this study, a stochastic method for estimating the probability function of the specific signal embedded in the additional noise is first theoretically proposed in a suitable form for the quantized level observation. Then, the effectiveness of the proposed method is experimentally confirmed by applying it to the observed data in the acoustic environment.