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The fundamentals of music theory outlined in the chapter are the building blocks for mashup and all music creation. Adjusting tempo and rhythm are discussed in s 7 and 9, and adjusting the pitch is covered in 8. Both key and tempo figure heavily in song selection, which is discussed in 6. Mashup form and structure are discussed in 11. But first, you ll need to learn a bit about how to use ACID software, which is in the next chapter.

After 1962, the Colombian discount rate rose steadily to reach 30% in 1979, a level that was more or less maintained through the 1980 s Colombian government bond yields were very high in 1931 1932, rising almost to 14% They declined thereafter, but did not go below.

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Breiman in what, from the discussion*, appears to have been received as quite a provocative paper, cautions against ignoring this work and the problem areas that gave rise to it. While agreeing with Breiman about the significance of working with the algorithmic modeling culture we would make one point in favor of the statistical discipline While these methodologies have been developed within the machine learning community, the contribution of the statistical community to a full understanding of these methodologies has been paramount. For example: decision trees were put on a firm foundation by Breirnan et al. (1984). They clarified the desirability of growing a large tree and then pruning it well as a number of other questions concerning the splitting criteria; neural networks were largely clarified by Cheng and Titterington (1994); Krzanowski and Marriott (1994); Bishop (1995a) and Ripley (1996) amongst others. The exaggerated claims made for MLP models prior to the intervention of statisticians no longer appear in the literature; boosting was dernystified by Friedman et al. (1998) and Friedman (1999,

Table 83 Mexican, Peruvian, and Colombian Interest Rates: Annual Averages, 1930 1989

efore you dive into making mashups, you ll need to gain some familiarity with the tools you ll use. This chapter is not intended to be a thorough discussion of each piece of software, rather just the subset of functions you will most likely use in constructing your mashup. The actual mashup construction will take place within Sony ACID, but some advanced audio editing functions will use Adobe Audition.

(continued)

2000) ;

ACID, formerly from the company Sonic Foundry, started out as a loopbased music construction tool. In early versions of the software, you could only drop in wav files that were pre-cut into repeatable loops. Soon ACID introduced beatmapping, the use of tempo and beat placement information, to handle longer sound files while independently manipulating tempo and pitch. In many ways, ACID has helped to spur the mashup revolution, and has long been the mashup artist s tool of choice.

8.17% in the 1930 s, and averaged 10.25%. In the 1940 s, the new series of 6% bonds sold at lower yields: 6.28 7.13%. In the early 1950 s, yields fluctuated in a similar range, which was well below the Peruvian, Chilean, and Brazilian bond yields. They were the highest reported here for Latin America in the early 1930 s and the lowest, except for Uruguay, in the early 1950 s, when reports cease.

support vector machines have yet to be widely investigated by statisticians, although Breiman (2001) and Hastie et al. (2001) have done a lot t o explain the workings of the algorithm. Brad Efron, in discussing Breiman s paper, suggests that it appears to be an argument for black boxes with lots of knobs to twiddle. This is a common statistical criticism of ML. It arises, not so much from the number of knobs on the black box, which is often comparable to the number of knobs in a statistical model3, but from the lack of a d a t a model. When we have a d a t a model, it gives confidence in the st,atistical work. The d a t a model arises from a n understanding of the process generating the d a t a and in turn assures us that we have done the job when the model is fitted to the data. There are then generally some diagnostic procedures that can be applied, such as examining the residuals. The expectation is that, as the data model matches the process generating the data, the residuals left over after fitting the model will be random with a n appropriate distribution. Should these prove to have a pattern, then the modelling exercise may be said to have failed (or to be as good as we can do). In the absence of a d a t a model, there seems little to prevent the process being reduced to an ad-hock empiricism with no termination ctiteria. However the ML community is frequently working in areas where no plausible d a t a model suggests itself, due to our lack of knowledge of the generating mechanisms.

TURN OF THE MILLENNIUM: 1990 2005

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