ABSTRACT
Tinnitus is a common clinical symptom. Researches have shown that fractal sound can effectively treat tinnitus. But current fractal sound is usually synthesized based on constant notes via fractal algorithm, which lead to monotony of synthesized fractal sound. So it is difficult to achieve personalized match. Clinical datas have confirmed that it is common to match tinnitus sound with nature sound and it has a good effect on regulating negative emotion and relieving tinnitus via some natural sound. Therefore, a new method of personalized synthesizing tinnitus rehabilitation sound based on iterative function system (IFS) fractal algorithm is proposed in this paper. This method firstly generates personalized audio library based on natural sound, then tinnitus rehabilitation sound is synthesized via IFS fractal algorithm. Simulation results show that rehabilitation sound in this paper can meet the basic requirements of tinnitus therapy sound and can match tinnitus sound by controlling personalized audio library. So it has reference significance to the treatment of tinnitus sound therapy.
ABSTRACT
Tinnitus is a common clinical symptom and its occurrence rate is high. It seriously affects life quality of the patients. Scientific researches show that listening some similar and none-repetitive music can relieve tinnitus to some extent. The overall music accorded with self-similarity character by the direct mapping method based on chaos. However, there were often the same tones continuous repeating a few times and tone mutations. To solve the problem, this paper proposes a new method for tinnitus rehabilitation sound synthesis based on pentatonic scale, chaos and musical instrument digital interface (MIDI). Experimental results showed that the tinnitus rehabilitation sounds were not only self-similar and incompletely reduplicate, but also no sudden changes. Thus, it has a referential significance for tinnitus treatment.
Subject(s)
Humans , Acoustic Stimulation , Music , Sound , Tinnitus , RehabilitationABSTRACT
The optimal model for the near-infrared spectroscopic analysis of serum glucose was established by partial least squares(PLS) and Savitzky-Golay(SG) smoothing method. Based on the prediction effect of the optimal single wave number model, a new dividing method for calibration set and prediction set was given. The calibration and prediction models were established by PLS method adopting the combination bands of 10000-5300 cm~(-1) and 4920-4160 cm~(-1) with Savitizky-Golay(SG) smoothing. By extending the number of smoothing points to 5, 7, ..., 87(odd) and polynomial degree to 2, 3, 4, 5, 6, fourteen smooth coefficient tables including 582 smooth modes were calculated. All PLS models corresponding to all smooth modes and all PLS factors(1-40) were constructed. The optimal model was selected by the prediction effect. And the derivation order was 1, the polynomial degree was 3 or 4, the number of smoothing points was 53, the optimal factor was 7 and the optimal RMSEP reach 0.376 mmol/L. The dividing method for calibration set and prediction set, the extending of SG smoothing modes, large-scale optimization combining SG smoothing modes and PLS factors can be effectively applied for the model optimization of near-infrared spectroscopic analysis.