The Experts below are selected from a list of 16440 Experts worldwide ranked by ideXlab platform
Hiroshi G Okuno - One of the best experts on this subject based on the ideXlab platform.
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musical instrument identification based on f0 dependent Multivariate Normal Distribution
International Conference on Acoustics Speech and Signal Processing, 2003Co-Authors: Tetsuro Kitahara, Masataka Goto, Hiroshi G OkunoAbstract:The pitch dependency of timbres has not been fully exploited in musical instrument identification. In this paper, we present a method using an F0-dependent Multivariate Normal Distribution of which mean is represented by a function of fundamental frequency (FO). This F0-dependent mean function represents the pitch dependency of each feature, while the F0-Normalized covariance represents the non-pitch dependency. Musical instrument sounds are first analyzed by the F0-dependent Multivariate Normal Distribution, and then identified by using the discriminant function based on the Bayes decision rule. Experimental results of identifying 6,247 solo tones of 19 musical instruments by 10-fold cross validation showed that the proposed method improved the recognition rate at individual-instrument level from 75.73% to 79.73%, and the recognition rate at category level from 88.20% to 90.65%.
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ICASSP (5) - Musical instrument identification based on F0-dependent Multivariate Normal Distribution
2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698), 2003Co-Authors: Tetsuro Kitahara, Masataka Goto, Hiroshi G OkunoAbstract:The pitch dependency of timbres has not been fully exploited in musical instrument identification. In this paper, we present a method using an F0-dependent Multivariate Normal Distribution of which mean is represented by a function of fundamental frequency (FO). This F0-dependent mean function represents the pitch dependency of each feature, while the F0-Normalized covariance represents the non-pitch dependency. Musical instrument sounds are first analyzed by the F0-dependent Multivariate Normal Distribution, and then identified by using the discriminant function based on the Bayes decision rule. Experimental results of identifying 6,247 solo tones of 19 musical instruments by 10-fold cross validation showed that the proposed method improved the recognition rate at individual-instrument level from 75.73% to 79.73%, and the recognition rate at category level from 88.20% to 90.65%.
Tetsuro Kitahara - One of the best experts on this subject based on the ideXlab platform.
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musical instrument identification based on f0 dependent Multivariate Normal Distribution
International Conference on Acoustics Speech and Signal Processing, 2003Co-Authors: Tetsuro Kitahara, Masataka Goto, Hiroshi G OkunoAbstract:The pitch dependency of timbres has not been fully exploited in musical instrument identification. In this paper, we present a method using an F0-dependent Multivariate Normal Distribution of which mean is represented by a function of fundamental frequency (FO). This F0-dependent mean function represents the pitch dependency of each feature, while the F0-Normalized covariance represents the non-pitch dependency. Musical instrument sounds are first analyzed by the F0-dependent Multivariate Normal Distribution, and then identified by using the discriminant function based on the Bayes decision rule. Experimental results of identifying 6,247 solo tones of 19 musical instruments by 10-fold cross validation showed that the proposed method improved the recognition rate at individual-instrument level from 75.73% to 79.73%, and the recognition rate at category level from 88.20% to 90.65%.
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ICASSP (5) - Musical instrument identification based on F0-dependent Multivariate Normal Distribution
2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698), 2003Co-Authors: Tetsuro Kitahara, Masataka Goto, Hiroshi G OkunoAbstract:The pitch dependency of timbres has not been fully exploited in musical instrument identification. In this paper, we present a method using an F0-dependent Multivariate Normal Distribution of which mean is represented by a function of fundamental frequency (FO). This F0-dependent mean function represents the pitch dependency of each feature, while the F0-Normalized covariance represents the non-pitch dependency. Musical instrument sounds are first analyzed by the F0-dependent Multivariate Normal Distribution, and then identified by using the discriminant function based on the Bayes decision rule. Experimental results of identifying 6,247 solo tones of 19 musical instruments by 10-fold cross validation showed that the proposed method improved the recognition rate at individual-instrument level from 75.73% to 79.73%, and the recognition rate at category level from 88.20% to 90.65%.
Boris Iglewicz - One of the best experts on this subject based on the ideXlab platform.
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on singular Multivariate Normal Distribution and its applications
Computational Statistics & Data Analysis, 1996Co-Authors: Koonshing Kwong, Boris IglewiczAbstract:Abstract The methods of evaluating the singular Multivariate Normal Distribution have been commonly applied even though the complete analytical proofs are not found. Recently, those evaluation methods are shown to have some errors. In this paper we present a new approach with a complete proof for evaluating the exact two-sided percentage points of a standardized m -variate Normal Distribution with a singular negative product correlation structure for m = 3 and with a singular negative equi-correlated structure for m ⩾ 3. The results are then applied to modify the existing procedures for estimating joint confidence intervals for multinomial proportions and for determining sample sizes. By extending the results from the Multivariate Normal Distribution to the Multivariate t -Distribution with the corresponding singular correlation structure, we obtain the corrected two-sided exact critical values for the Analysis of Means for m = 4,5.
Koonshing Kwong - One of the best experts on this subject based on the ideXlab platform.
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on singular Multivariate Normal Distribution and its applications
Computational Statistics & Data Analysis, 1996Co-Authors: Koonshing Kwong, Boris IglewiczAbstract:Abstract The methods of evaluating the singular Multivariate Normal Distribution have been commonly applied even though the complete analytical proofs are not found. Recently, those evaluation methods are shown to have some errors. In this paper we present a new approach with a complete proof for evaluating the exact two-sided percentage points of a standardized m -variate Normal Distribution with a singular negative product correlation structure for m = 3 and with a singular negative equi-correlated structure for m ⩾ 3. The results are then applied to modify the existing procedures for estimating joint confidence intervals for multinomial proportions and for determining sample sizes. By extending the results from the Multivariate Normal Distribution to the Multivariate t -Distribution with the corresponding singular correlation structure, we obtain the corrected two-sided exact critical values for the Analysis of Means for m = 4,5.
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Evaluation of One-Sided Percentage Points of the Singular Multivariate Normal Distribution
Journal of Statistical Computation and Simulation, 1995Co-Authors: Koonshing KwongAbstract:This paper presents a new theorem, as a substitute for existing results which are shown to have some errors, for evaluating the exact one-sided percentage points of the Multivariate Normal Distribution with a singular negative product correlation structure. By extending the result from the Multivariate Normal Distribution to the Multivariate t-Distribution with corresponding singular correlation structure, we tabulate the one-sided critical points for the Analysis of Means procedure.
Masataka Goto - One of the best experts on this subject based on the ideXlab platform.
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musical instrument identification based on f0 dependent Multivariate Normal Distribution
International Conference on Acoustics Speech and Signal Processing, 2003Co-Authors: Tetsuro Kitahara, Masataka Goto, Hiroshi G OkunoAbstract:The pitch dependency of timbres has not been fully exploited in musical instrument identification. In this paper, we present a method using an F0-dependent Multivariate Normal Distribution of which mean is represented by a function of fundamental frequency (FO). This F0-dependent mean function represents the pitch dependency of each feature, while the F0-Normalized covariance represents the non-pitch dependency. Musical instrument sounds are first analyzed by the F0-dependent Multivariate Normal Distribution, and then identified by using the discriminant function based on the Bayes decision rule. Experimental results of identifying 6,247 solo tones of 19 musical instruments by 10-fold cross validation showed that the proposed method improved the recognition rate at individual-instrument level from 75.73% to 79.73%, and the recognition rate at category level from 88.20% to 90.65%.
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ICASSP (5) - Musical instrument identification based on F0-dependent Multivariate Normal Distribution
2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698), 2003Co-Authors: Tetsuro Kitahara, Masataka Goto, Hiroshi G OkunoAbstract:The pitch dependency of timbres has not been fully exploited in musical instrument identification. In this paper, we present a method using an F0-dependent Multivariate Normal Distribution of which mean is represented by a function of fundamental frequency (FO). This F0-dependent mean function represents the pitch dependency of each feature, while the F0-Normalized covariance represents the non-pitch dependency. Musical instrument sounds are first analyzed by the F0-dependent Multivariate Normal Distribution, and then identified by using the discriminant function based on the Bayes decision rule. Experimental results of identifying 6,247 solo tones of 19 musical instruments by 10-fold cross validation showed that the proposed method improved the recognition rate at individual-instrument level from 75.73% to 79.73%, and the recognition rate at category level from 88.20% to 90.65%.
