Sample Qin #4 – 10 Year Old Beginner Qin w/ Longren Binxian Strings


Here you will find data for Sample Qin #4, which is a 10 year old beginner qin, strung with Longren Binxian nylon composite guqin strings. This dataset is the first full dataset that was externally recorded by the owner of the qin, and sent to myself for analysis, as opposed to me directly recording the qin myself. As it is difficult to always have access to qin for recording first hand, it will become necessary to start collecting and analyzing various recordings generously provided to me by qin players. This qin was recorded particularly for several comparison studies, focusing on strings 5, 6, and 7 in particular. These strings are of importance since they will be compared to my own handmade experimental twisted nylon “Fire Strings”, which I have so far only developed for strings 5, 6, and 7, and were also strung and recorded on this qin as, as well as my own personal qin. The second comparison will be again between strings 5, 6, and 7, except using Suxin silk strings on this qin and Sample Qin #3. This qin will also be compared with the two datsets collected for my own personal qin strung with two different sets of Longren Binxian strings.

The graphs below are the harmonic content data, spectrograms, and autocorrelation graphs for the tested Sample Qin #4 strung with Longren Binxian strings, with the specified tuning and technique for each data set. You can enlarge the images by clicking on the thumbnails. At the bottom of the page is a brief description of each set of data.


DATA

1. Linear Spectrum Harmonic Content Graphs

 

2. Autocorrelation

 

3. Spectrograms (Window 4096)

 

4. Spectrograms (Window 2048)

 

5. Spectrograms (Window 512)


DATA DESCRIPTIONS

  1. Linear Spectrum Harmonic Content Graphs – Shows the harmonic content of each string, graphed along the linear spectrum in terms of frequency to intensity. A very accurate way to easily visualize the harmonics and overtones of each string.
  2. Autocorrelation – Shows the periodic nature or trends from a given set of data. Autocorrelation can provide a unique look at data, and can reveal repeating patterns from seemingly random datapoints. For this application, it is derived from the original signal and more clearly shows the decaying oscillatory nature of the plucked string.
  3. Spectrograms (Window 4096) – Shows the spectrogram of each string, with a window setting of 4096. This setting allows one to clearly view all of the harmonics by showing the frequency, intensity, and duration of each harmonic. This graph can be most easily cross-correlated to the linear spectrum harmonic content graphs to compare durations and intensities of harmonics in a string.
  4. Spectrograms (Window 2048) –  Shows the spectrogram of each string, with a window setting of 2048. For this application, I have found that this setting is ideal in viewing the oscillatory instabilities of the guqin string more clearly, which cannot be seen as well in higher window settings. These are seen as wavering lines, which are most noticeably present in the mid-upper harmonics.
  5. Spectrograms (Window 512) – Shows the spectrogram of each string, with a window setting of 512. For this application, I have found that this setting, while having the lowest frequency band resolution of the three settings, allows one to zoom out on the entirety of the harmonic spectrum, and see how the overall power level and intensity shifts from one string to another, and where the harmonic content is overall most present for a given string.