Note:

The application of information theory to music may provide both a means for measuring the information content of the messages .of the system and for studying the effects of such messages in the field of psychology of music (e.g., the group at Iowa University which is carrying on work commenced by Carl Seashore 3 ). Before the techniques of information theory may be applied to a study of music much statistical data about the music system must be compiled. Masking data must be compiled dealing with many simultaneous tones as are encountered in music; the effects of timbre in overcoming or adding to masking effects must be investigated; the effects of masking on and by the singing voice must be tabulated; and similar data must be compiled dealing with the effects of other types of tonal interactions. This data of the effects of tonal interactions may be used both to reduce the effects of such auditory phenomena and to determine which components of a message pass through the communication channel (i.e., the ear). The masking problem, as illustrated in Figures 19 and 20, may be so acute in some compositions that the part of the signal carrying the information content may be obliterated. These illustrations indicate, however, that effects of masking and other forms of tonal interactions may be reduced This study has indicated, further, that elimination of the effects of tonal interactions is impossible if a wide range of dynamic intensities is used, but that these effects are limitations of the transmission channel and thus restrict the choice of tones of the musical system which may be used at one time.

Please read the abstract in the section 00front of this document / Thesis (DMus)--University of Pretoria, 2007. / Music / unrestricted

Graduate

The present study shows that prestructuring based on domain knowledge leads to statistically significant generalization-performance improvement in artificial neural networks (NNs) of the multilayer perceptron (MLP) type, specifically in the case of a noisy real-world problem with numerous interacting variables. The prestructuring of MLPs based on knowledge of the structure of a problem domain has previously been shown to improve generalization performance. However, the problem domains for those demonstrations suffered from significant shortcomings: 1) They were purely logical problems, and 2) they contained small numbers of variables in comparison to most data-mining applications today. Two implications of the former were a) the underlying structure of the problem was completely known to the network designer by virtue of having been conceived for the problem at hand, and b) noise was not a significant concern in contrast with real-world conditions. As for the size of the problem, neither computational resources nor mathematical modeling techniques were advanced enough to handle complex relationships among more than a few variables until recently, so such problems were left out of the mainstream of prestructuring investigations. In the present work, domain knowledge is built into the solution through Reconstructability Analysis, a form of information-theoretic modeling, which is used to identify mathematical models that can be transformed into a graphic representation of the problem domain's underlying structure. Employing the latter as a pattern allows the researcher to prestructure the MLP, for instance, by disallowing certain connections in the network. Prestructuring reduces the set of all possible maps (SAPM) that are realizable by the NN. The reduced SAPM--according to the Lendaris-Stanley conjecture, conditional probability, and Occam's razor--enables better generalization performance than with a fully connected MLP that has learned the same I/O mapping to the same extent. In addition to showing statistically significant improvement over the generalization performance of fully connected networks, the prestructured networks in the present study also compared favorably to both the performance of qualified human agents and the generalization rates in classification through Reconstructability Analysis alone, which serves as the alternative algorithm for comparison.