Sample Qin #4 – Experimental Nylon Fire Strings 5, 6, and 7

Here you will find the datasets for Sample Qin #4, a 10 year old beginner qin, strung with some of my experimental twisted nylon qin strings, dubbed “Fire Strings”, for strings 5, 6, and 7. I sent out a set of my current top best strings of this type for 5, 6, and 7 to the owner of this qin to try out and provide feedback. As part of this effort to improve synthetic qin strings, these strings were recorded on this particular qin by it’s owner and sent to me for analysis. I will be comparing the response of these strings on this qin with the response of the popular Longren Binxian strings also strung and recorded on this qin, as well as data collected from my own personal qin using both Longren strings as well as my own experimental strings. These comparisons will be done as several of my case studies, which you can find by clicking on the link to the Harmonic Analysis Case Studies page. Already, these experimental twisted nyon strings are showing to have some very interesting and unique patterns and features as opposed to standard monofilament core strings, and open the door for new possibilities and potential in the world of synthetic qin strings.

The graphs below are the harmonic content data, spectrograms, and autocorrelation graphs for the tested Sample Qin #4 strung with experimental twisted nylon strings (“Fire Strings”) 5, 6, and 7, 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.


1. Linear Spectrum Harmonic Content Graphs


2. Autocorrelation


3. Spectrograms (Window 4096)


4. Spectrograms (Window 2048)


5. Spectrograms (Window 512)


  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.