Abstract: In order to appropriately set MSm analysis conditions, an MSm-1 analysis executer (51) makes a mass spectrometer (20) perform an MSm-1 analysis (where m is an integer from 2 to n) to acquire three-dimensional data showing an intensity for each of the N m/z values and each of the M retention times (where N and M are natural numbers). Based on the three-dimensional data, a data matrix creator (41) creates data matrix X in which intensity data are arranged in N rows which differ from each other in m/z value and M columns which differ from each other in retention-time value. A matrix factorization executer (42) determines an N×K spectrum matrix S and K×M profile matrix P (where K is a natural number) by matrix factorization based on data matrix X so that this matrix X is approximated by product SP of the matrices S and P. An m/z detector (43) detects m/z of a precursor ion originating from a sample component from the values of the matrix elements in each column of matrix S.

Abstract: Provided is a method for determining a first N×K matrix S and second K×M matrix P, using factor number K, so that their product SP approximates to an N×M data matrix X obtained by analyzing a sample containing an unknown number of components. Multiple candidates of regularization parameter ?r and one sparsity-inducing regularization function R(S,P) are prepared. For each regularization-parameter candidate ?r, a candidate Sr of matrix S and candidate Pr of matrix P which minimize a loss function L(S,P)=D(X|SP)+?rR(S,P) are determined, where D(X|SP) is a distance function expressing the degree of difference between X and SP. For each combination of matrix element Xnm in X and corresponding matrix element (SrPr)nm in SrPr, a transformed value ynm=Fnm(Xnm(SrPr)nm) is determined using function Fnm which performs variable transform from probability distribution Pnm corresponding to D(Xnm|(SP)nm) into common probability distribution Pcommon, and goodness of fit between ynm and Pcommon is calculated.

Abstract: A system for multifaceted singing analysis for retrieval of songs or music including singing voices having some relationship in latent semantics with a singing voice included in one particular song or music. A topic analyzing processor uses a topic model to analyze a plurality of vocal symbolic time series obtained for a plurality of musical audio signals. The topic analyzing processor generates a vocal topic distribution for each of the musical audio signals whereby the vocal topic distribution is composed of a plurality of vocal topics each indicating a relationship of one of the musical audio signals with the other musical audio signals. The topic analyzing processor generates a vocal symbol distribution for each of the vocal topics whereby the vocal symbol distribution indicates occurrence probabilities for the vocal symbols. A multifaceted singing analyzing processor performs analysis of singing voices included in musical audio signals, in the multifaceted viewpoint.

Abstract: A system for multifaceted singing analysis for retrieval of songs or music including singing voices having some relationship in latent semantics with a singing voice included in one particular song or music. A topic analyzing processor uses a topic model to analyze a plurality of vocal symbolic time series obtained for a plurality of musical audio signals. The topic analyzing processor generates a vocal topic distribution for each of the musical audio signals whereby the vocal topic distribution is composed of a plurality of vocal topics each indicating a relationship of one of the musical audio signals with the other musical audio signals. The topic analyzing processor generates a vocal symbol distribution for each of the vocal topics whereby the vocal symbol distribution indicates occurrence probabilities for the vocal symbols. A multifaceted singing analyzing processor performs analysis of singing voices included in musical audio signals, in the multifaceted viewpoint.

Abstract: A musical piece recommendation system that allows instantaneous registration of a new user and a new musical piece without retraining in a basic training section. A first incremental training section monitors a rating history storage section, and each time a change is made to a rating history or a new user is added, performs updating of or addition of the topic selection probability for the user for which the change is made to the rating history or for the new user such that the likelihood determined by a basic training section is kept maximized. A second incremental training section monitors an acoustic feature storage section, and each time a new musical piece is added to perform addition to acoustic features, adds the musical piece selection probability related to the added musical piece such that the likelihood determined by the basic training section is kept maximized.

Abstract: A musical piece recommendation system is provided that allows instantaneous registration of a new user and a new musical piece without retraining in a basic training section. A first incremental training section 21 monitors a rating history storage section 3, and each time a change is made to a rating history or a new user is added, performs updating of or addition of the topic selection probability for the user for which the change is made to the rating history or for the new user such that the likelihood determined by a basic training section 17 is kept maximized. A second incremental training section 21 monitors an acoustic feature storage section 5, and each time a new musical piece is added to perform addition to acoustic features, adds the musical piece selection probability related to the added musical piece such that the likelihood determined by the basic training section 17 is kept maximized.

Abstract: An apparatus and method for extracting a predetermined non-harmonic structured spectral component contained in an audio signal. Then, the extracted predetermined spectral component is increased or decreased. In this process, the spectrum of the audio signal is calculated by frequency analysis, so that a spectrum component corresponding to the predetermined non-harmonic structured spectral component is extracted and then increased or decreased. The extraction of the predetermined non-harmonic structured spectral component is performed with reference to a spectral component of a template stored in advance. In this process, the spectral component of the template is adapted in such a manner that the difference between the extracted spectral component and the spectral component of the template goes below or at a predetermined value.