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  1. When adjusting a violin one big problem is the mass if data if one tries to do some "objective" measurements. Look at any reasonable spectrum done at high resolution. You will see a "wood of frequency peaks". How to interpret those peaks is another matter . Anders Buen has written some articles about the Dünnwald parameters where three numbers are calculated and those numbers are said to provide some kind of classification of classic Cremonensic instruments (put in one bin) and modern instruments that will go into another fairly distinct bin. Notice that nothing is said about "quality" . The idea of this thread is: Has somebody tried to use the Dünnwald parameters as tools for instrument optimization? My experience is that it helps a lot to look at only three numbers instead of tens of peaks. It is also much easier to see when an adjustment will move the instrument being worked on towards some easy to interpret "goal". What have I done: I record a G-major scale played from low G upwards to B on the E-string. Much vibrato is used because Dünnwalds parameters are based on recordings of real music. Next I generate a spectrum (4096 points) and export the result as a plain text file that can be processed further. I run a small program, written in Python, that calculates the Dünnwald parameters for the adjustment round (see below). Based on the parameters it is easy to see how the last modification influenced the instrument. It also feels like the changes go in the correct direction and when the parameters improve the sound of the instrument also improves. An example: I recently started a round of optimization on my own Harding fiddle that I use myself. The initial state was: ./Dunn_A.py 00_hard_initial.txt File to process: 00_hard_initial.txt Dunnwald parameters for :00_hard_initial.txt A = 57.9133410465 B = 54.39818555 C = 50.5321468125 D = 48.6583449425 E = 45.1510729073 F = 37.1172910121 L[Db] = -14.525938 ACD - B = -2.91313386481 DE - F = 9.31585192488 After seventeen steps of internal sanding the sound had improved very much and so had the Dünnwald parameters: ./Dunn_A.py 17_har_btn_uppe_esidan_balans.txt File to process: 17_har_btn_uppe_esidan_balans.txt Dunnwald parameters for :17_har_btn_uppe_esidan_balans.txt A = 57.2790034651 B = 55.5393324333 C = 52.5985449688 D = 51.3234882299 E = 49.7805976623 F = 39.7953643065 L[Db] = -3.600813 ACD - B = -2.3831962358 DE - F = 10.5492311683 The final situation can be compared to for example the Stradivarius ”Hellier” från 1679 : L(dB) = -6,0 ACD-B = -1,2 DE-F = 10,3 Generally higher values within reason are thought to be better. My original article about adjusting the Harding fiddle (in Swedish) is found here http://larsil2009.wordpress.com/2015/03/19/dunnwaldparametrar-som-hjalp-vid-fioltrimning/ Anders Buens article used as the base for the experiment is found here: http://www.google.fi/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0CCQQFjAA&url=http%3A%2F%2Fwww.akutek.info%2FPapers%2FAB_Timbre_Parameters.pdf&ei=A7oKVcCUBYzqaKfdgcgE&usg=AFQjCNF4h1UWWcqbdVWNCUE0PxQR-twRsw&bvm=bv.88528373,d.d2s This is how the amplitudes of the frequency ranges used to calculate the Dnwald parameters changed during the optimization process. Notice that the range seems to be roughly 10 dB. Comments or ideas?
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