Table 4.6 contains all genres or styles that were identified by participants, including styles supposedly identifying particular composers. In the table these are associated with the root genres found by the automated classifier to give some sense of compar- ison (see section 4.4). Plotting the ratio of the occurrences of root genres in table 4.6 against the classifier results in figure 4.3 is tempting, but this would not be partic- ularly legitimate because the listening survey used only six samples and the genres identified by humans were mostly assigned to the group as a whole which contained both harmonies and melodies. However, it is clear that the vast majority of comments on genre and style fell within the bounds of Western Classical music, and this is in striking concordance with the results of section 4.4.6.
If the attention is instead focussed within the genre of Western Classical music, which is an extremely broad genre, then the participant responses in table 4.6 do sug- gest a fair level of stylistic diversity which could perhaps not be captured by the mod- est collection of Western Classical sub-genres in McKay’s taxonomy (see figure 4.2).
Table 4.6: Genres suggested by participants
Classifier root genre Genre or style identified
Jazz Jazz
Western Folk Folk
Disney Western Classical Sibelius Chopin Late Romantic Impressionist Perpetuum Mobile Shostakovich Post-1920 Classical Atonal Stravinksy
Expressive tonal music Non-traditional
Art Music Minimalist Etude Bartok
Western Tonal Classical 20th Century
Modern Pop
Pop Muzak New-age
Rock Progressive Rock
4.6
Discussion
The automated Schillinger System’s output has been evaluated using methods which are intended to improve upon those currently present in computer music literature. The stylistic diversity of a group of 200 output samples has been measured using an automated genre classification system. The intrinsic musical merit of a group of six selected output samples, rendered with human performances, has been rigorously assessed by a group of expert human participants.
The results from the listening experiment are convincing. Collectively, the listen- ers registered positive responses regarding the music’s merit; in particular its likeabil- ity and interestingness. They decided that the music’s level of predictability was close to appropriate, and that there was some form of logic underlying its construction; al- though in these cases there was slightly less consensus. The application of a method of qualitative analysis from Grounded Theory revealed a multitude of complaints and compliments specific to various properties of the samples, which have provided a wealth of information to inform further development. Ultimately these contributed
§4.6 Discussion 93
to an overall positive opinion of the system’s output.
The classification experiment suggested that the harmonies fell within the sweep- ing genre of Western Classical music, while the melodies were a somewhat more di- verse split among Western Classical, Jazz, and Rhythm and Blues. These results are corroborated quite strongly by the list of styles and genres the human participants attributed to the samples in the listening experiment. These latter styles, however, do represent diversity within the genre of Western Classical music, showing the potential for the automated Schillinger System to be applied to a variety of musical contexts.
Additional experiments will be needed to address some lingering questions. For instance, it is unclear how much of an influence the quality of the audio rendering may have had on listeners’ perceptions of musical merit, or how much the choice of instrumentation influenced their interpretations of style. McKay and Fujinaga have suggested that instrumentation is a particularly important feature for automatically distinguishing genre [Mckay and Fujinaga 2005]. On the other hand, Aucouturier and Pachet found that for humans, style and timbre may not be so strongly correlated [Aucouturier and Pachet 2003]. It would almost certainly be unwise to revert to pre- senting raw MIDI data to an audience, but it could be informative to perform a similar experiment using high quality recordings limited to a single instrument.
Chapter 5
Conclusion
The Schillinger System of Musical Composition was intended to be used by students of composition and by working composers. Despite its self-proclaimed grounding in a school of thought that espoused rigorous scientific approaches to all forms of human endeavour, it was ultimately designed to stimulate real creativity in musical thinking. Schillinger almost certainly did not conceive of the formalism as a means of generating music automatically; in fact he stated quite plainly that success using his methods depended on “the ability to think” [Schillinger 1978]. Such a statement should not act as a deterrent, but it does force one to accept that an extensive formalism intended for the composition of new music cannot be so rigorous that it presents itself as a complete mathematical framework for computer implementation. The issues encountered in building the automated Schillinger System, as described in chapter 3, were therefore to be expected, and by necessity the resolutions of these issues required a modicum of creativity on the author’s part.
Rader stated the opinion that the goal of computer music is not to be aesthetically ‘perfect’, but to be indistinguishable from human-produced music [Rader 1974]. This goal has since been mostly superseded by the idea, echoed by Blackwell, that
the goal of automated composition research is not to replace human music making with an automatic machine . . . the desire is to find artificial music that is different from human expression, yet comprehensible [Blackwell 2007].
This line of thinking influenced the decision to use a listening experiment in this research to establish the intrinsic musical merit of the automated Schillinger Sys- tem’s output, rather than attempt to bluff audiences with selections of human- and computer-composed pieces in the manner of Storino et al. [Storino et al. 2007]. It is also in concordance with many authors’ views that the ultimate goal of algorith- mic composition should be to realise genuinely new music, rather than ‘recompose’ existing music.