Chirp signal is a typical non-stationary signal, and have been widely used in communication, sonar, radar and so on. So, this signal is worth to analysis. In order to show the characteristics, this paper first introduces the definition and formula of each algorithm, then with all kinds of time-frequency analysis method to the signals, and the signal to add two sine signal noise are analyzed, the comparison of the characteristics of the method in the paper, and the signal for the analysis, the selection of an appropriate analysis. Through analysis and comparison, when dealing with the signal, Hilbert-Huang transformation not only has a better gathered characteristic, but also has a better resolution to distinguish the sine signal noise. Finally, use the MATLAB software simulation to obtain the result.
Compared with common psychoacoustic model, this article uses wavelet packet decomposition to decompose a signal. This method improves the situation of insufficient time-frequency resolution which the uniform spectrum analysis causes. In addition, frequency division by wavelet packet decomposition is much closer to human’s critical band than the way common psychoacoustic model getting, it is more suitable to human’s hearing characteristics. So we can use wavelet packet decomposition replace FFT in MPEG, and get accurate psychoacoustic model.
We first introduce multiscale power (MSP) method to assess the power distribution of physiological signals on multiple time scales. Simulation on synthetic data and experiments on heart rate variability (HRV) are tested to support the approach. Results show that both physical and psychological changes influence power distribution significantly. A quantitative parameter, termed power difference (PD), is introduced to evaluate the degree of power distribution alteration. We find that dynamical correlation of HRV will be destroyed completely when PD>0.7.
When re-quantize a signal of low levels, serious distortion may occur due to word length reduction. Adding proper dither before re-quantization can improve the quantization error characteristic, while it will increase overall noise. By applying noise shaping, transposing most of the noise power to the band which human ear is less sensitive can reduce audible noise and thus improve auditory sense. This paper firstly discussed the applications of dither and noise shaping in professional digital audio, according to human ear’s psychoacoustic properties, by adopting genetic algorithm, fulfilled the design of psycho-acoustically optimal noise shaping filter. Then it applied the noise shaping technology to upsampling processing, realized the optimal noise shaping filter design under oversampling condition. Finally, it performed algorithm simulation on mat lab platform. Actual listening test confirms that by combining noise shaping technology with upsampling technology, sound quality can be improved significantly.
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