Above are papers related to the following paper: [Paper_ID] Paper #1130069. 	[Title] ANALYSIS OF SOUND TEXTURES IN MUSICAL AND MACHINE SOUNDS BY MEANS OF HIGHER ORDER STATISTICAL FEATURES. [Abstract] In this paper we describe a sound classification method, which seems to be applicable to a broad domain of stationary, non-musical sounds, such as machine noises and other man made non periodic sounds. The method is based on matching higher order spectra (HOS) of the acoustic signals and it generalizes our earlier results on classification of sustained musical sounds by higher order statistics. An efficient "decorrelated matched filter" implemetation is presented. The results show good sound classification statistics and a comparison to spectral matching methods is also discussed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #617378. 	[Title] Testing for Gaussianity and Non Linearity in the sustained portion of musical sounds. [Abstract] Higher order spectra of a signal contain information about the non Gaussian and non Linear properties of the system that created it. Since the non linearity in musical signal usually originate in the excitation signal while the linear spectral characteristics are attributed to the resonant chambers, we discard the spectral information by looking at the higher order statistical properties of the residual signal, i.e. the estimated input signal obtained by inverse filtering of the sound. In the current paper we show that the skewness and kurtosis values of the residual could be used for characterization of such important sound properties as belonging to families of strings, woodwind and brass instrumental timbres. The skewness parameter is shown to be closely related to the bicoherence function calculated over the original signal and as such it is succinct to an interpretation as statistical test for the signal conforming to a linear non Gaussian model. The above results are compared to the Hinich bispectral tests for Gaussianity and non Linearity of time series and exhibit a similar classification results. Finally, regarding the higher order statistics of a signal as a feature vector, a statistical distance measure for the cumulant space is suggested. Please summarise all the given information.

Paper #1130069 has the title `ANALYSIS OF SOUND TEXTURES IN MUSICAL AND MACHINE SOUNDS BY MEANS OF HIGHER ORDER STATISTICAL FEATURES.  
` and the abstract `In this paper we describe a sound classification method, which seems to be applicable to a broad domain of stationary, non-musical sounds, such as machine noises and other man made non periodic sounds. The method is based on matching higher order spectra (HOS) of the acoustic signals and it generalizes our earlier results on classification of sustained musical sounds by higher order statistics. An efficient "decorrelated matched filter" implemetation is presented. The results show good sound classification statistics and a comparison to spectral matching methods is also discussed. 
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Question: Which category should Paper #1130069 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1130069 has the title `ANALYSIS OF SOUND TEXTURES IN MUSICAL AND MACHINE SOUNDS BY MEANS OF HIGHER ORDER STATISTICAL FEATURES.  
` and the abstract `In this paper we describe a sound classification method, which seems to be applicable to a broad domain of stationary, non-musical sounds, such as machine noises and other man made non periodic sounds. The method is based on matching higher order spectra (HOS) of the acoustic signals and it generalizes our earlier results on classification of sustained musical sounds by higher order statistics. An efficient "decorrelated matched filter" implemetation is presented. The results show good sound classification statistics and a comparison to spectral matching methods is also discussed. 
`.
Question: Which category should Paper #1130069 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks