Authorship And Attribution In Early Thesis

Length: 14 pages Sources: 1+ Subject: Music Type: Thesis Paper: #20745555 Related Topics: Ludwig Van Beethoven, Romantic Period, Grieving Process, Classical Music
Excerpt from Thesis :

A certain feeling toward propriety and morality is stamped upon our sex, which does not allow us to appear alone in public, nor without an escort. Thus how can I present my musical work, to the public with anything other than timidity. The work of any lady…can indeed arouse a degree of pity in the eyes of some experts." (Bowers and Tick, 1987)

Bowers and Tick state that many composers of this time "Reichardt, Hensel, and Schumann -- published lieder under male authorship. A few of Reichardt's early songs were included in a collection of her father's lieder, 'Duetsche Lieder' and three of Hensel's early songs "appear in each of Felix's Opus 8 and Opus 9; the 'Allegemeine musikalische Zeitung' claimed that 'An des lust'gen Brunnenes Rand' a duet composed by Fanny, is the best song in the collection" of Opus 8. Additionally three of Schumann's lieder were "included in a collection of twelve songs published under the joint names of Robert and Clara Schumann." (Bowers and Tick, 1987) So it was that women first published under the names of their father's and husbands prior to possessing the self-confidence and freedom within society to express themselves through compositions which they dared to publish in their own names.

IV. Composer Identification Based on Typical Pattern Detection

The work of Salas and Gelbukh (nd) entitled: "Musical Composer Based on Detection of Typical Patterns in a Human Composer's Style" states that the advance in informational technology and the use of computational tools "make it possible to develop models of music in order to automate composition processes and eventually build systems that automatically generate music." (nd) the following are stated to be possible applications of such systems:

(1) Creation of new styles of music by finding patterns of different styles and mixing them, helping people to compose music.

(2) Providing tools that allow the user to edit the compositions generated by the system, resulting in a user composition, but which contains patterns that great musicians create and thus, people without musical knowledge can make good music.

(3) Enabling computers have the capacity to carry out a process until now reserved for humans. Making this, machines will get human characteristics creating another way of human-machine communication.

(4) Breaking the monopoly of large music companies that currently decide what music is to be listened to.

(5) Having machinery for the generation of live music for restaurants, offices, shops, etc. with compositions created in real time by indefatigable musicians.

(6) Providing tools that allow children from a very young age to have direct contact with the process of musical composition, which according to studies stimulates the mind for better performance in all other human activities. (Salas and Gelbukh, nd)

Salas and Gelbukh state that it is very important to gain and understanding that will result in the generation "as much resemblance to the examples with which the system is trained 'when using supervised learning techniques." (Salas and Gelbukh, nd) One of the primary aspects of a melody are characteristics relating to the "pattern of the sequences of notes used for each author. These patterns of transition can be measured statistically to determine the probability of moving from one state to another in a Markov process, what can be used to generate music which reflects these patterns of transition making it similar." (Salas and Gelbukh, nd)

Salas and Gelbukh report that the evolving computerized composition systems have "proved to be a versatile tool that can be used to model various phenomena of reality. In the case of our system we have obtained satisfactory results and the opportunity to hear compositions forever different. The development of this work is open as part of an overall process...

...

For a first study over 300 different compositions of Bach, Handel, Telemann, Mozart and Haydn were used and from this data set it was shown that even with a few features, the styles of the various composers could be separated with leave-one-out-error rates varying from 4% to 9% with the exception of the confusion between Mozart and Haydn which yielded a leave-one-out-error rate of 24%. A second experiment included 30 fugues from J.S. Bach, W.F. Bach and J.L. Krebs, all of different style and character. With this data set of compositions of undisputed authorship, the F minor fugue for organ, BWV 534 (of which Bach's authorship is disputed) then was confronted. It could be concluded that there is experimental evidence that J.L. Krebs should be considered in all probability as the composer of the fugue in question." (Backer and Kranenburg, 2004)

The work of Liu (2002) entitled: "Modeling music as Markov chains - composer identification" reports a research study in which it is posited that music "…can be thought of as random processes, and music style recognition can be thought of as a system identification problem. Then, a general framework for modeling music using Markov chains is described, and based on this framework, a two-way composer identification scheme is demonstrated." (Liu, 2002) This work reports a study in which the statistical reliability of human listening skills ensures the correct identification of the composer of a work will be examined.

Liu (2002) relate that this scheme is one that "…utilizes the Kullback-Leibler distance as the metric between distribution functions, and it is shown that under the condition when the marginals are identical, the scheme gives maximum likelihood identification." (2002)

Computational models of music styles are addressed and it is related that the steps of building a Markov chain model for a music style include the following steps:

Step 1: Define the repertoire of that style.

Step 2: Encode all the works of that repertoire.

Step 3: Define the state space that consists of musical events.

Step 4: From all the encoded works available in that repertoire, calculate the matrix of conditional event-transition probabilities.

Step 5: Let be the Markov chain corresponding to {P, S}. Call Ca a Markov chain model for that particular style of music. (2002)

According to Liu, Step 1 can be difficult because "musical styles are often of a vague notion to human beings. Is Beethoven's Moonlight Sonata classical or romantic? Here, it is supposed that the human researcher somehow clearly define the repertoire, anyway." (2002) as well, Liu state that the second step "…is not quite straightforward, either. It all depends on what musical features are relevant. Data-Entry is another practical difficulty in this step, because it takes forever if the repertoire is huge." (2002)

The third step is referred to be Liu as "huge" and one that involves "…defining an interesting prominent feature associated with that style, and quantifying that feature in a clever way. The state space consists of all the possible values to which the feature is quantified." (2002) Finally Liu state that the fourth step or the 'transmission matrix' "…is obtained by normalizing the histogram of state-transition instances in all the works of that repertoire." (2002)

Selfridge -- Field and Sapp relate that the chain models of Markov "..are built up for Mozart's and Haydn's scale degree class transitions" and that the repertoire is comprised by the following:

(1) all of their string quartets,

(2) 100 movements from Mozart and (3) 212 movements from Haydn.

(4) the scale degree is defined relative to the tonics, i.e., all works are transposed into the same key.

(5) Also, for the sake of reducing the dimensionality of transition matrix, the system does not distinguish between the same degree class at different octaves. The system does not distinguish between major keys and minor keys, either.

(6) Then, the two-way identification tests based on Kullback-Leibler distances were conducted for each of…

Sources Used in Documents:

Bibliography

Backer, Eric and Kranenburg, Peter van (2004) on Musical Stylometry- a Pattern Recognition Approach. Science Direct 2004 Elsevier.

Bowers, Jane M. And Tick, Judith (1987) Women Making Music: The Western Art Tradition, 1150-1059. University of Illinois Press, 1987.

Haynes, Bruce (2007) the End of Early Music. Oxford University Press. U.S., 2007.

Kranenburg, Peter van (2006) Composer Attribution by Quantifying Compositional Strategies. University of Victoria 2006.


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