Five Rather Simple Hints Suitable For SAR1B Unveiled

2007AA042105), the Natural Science Foundation of Heilongjiang Province, China (Grant No. F2015008), and the State Key Laboratory of Robotics and System (HIT) (No. SKLRS201502C). Appendix Appendix A Under a silent period of the speech signal, only complicated environmental noises are collected. Thus, to determine the silence signal or speech signal, using endpoint detection is necessary. In general, the speech BMS-354825 signal is non-stationary, but it can be assumed stationary for short time scales (from 10 ms to 30 ms). Therefore, the speech signal is divided into overlapping frames. The frames are then windowed using an analysis window function. Relying on this characteristics, a short time SAR1B energy and a short time zero crossing rate [19,20] can be used for the endpoint detection. The short time energy [19] is defined as: E=��m=n?N+1n[x(m)w(n?m)]2n?N+1��m��n (A.1) where expression E represents the energy of the signal x(m), w(n ? m) is the window function and N is the window length. In this paper, a Hamming window that has a window length of 20 ms is employed as the analysis window function. High energy would be classified as voice and lower energy as silence, namely setting the threshold to classify the speech as voice or silence. If the calculated signal energy is lower than the threshold, the speech is classified as silence, whereas if the energy is more than the threshold, the speech is classified as voice. In addition, the zero crossing rate (ZCR) counts the number of zero crossings in the speech signal. Voiced segments have a low ZCR compared with unvoiced segments. The definition of the find more short time zero crossing rate is as follows: z=��m=?�ޡ�|sgn[x(m)]?sgn[x(m?1)]|w(n?m) (A.2) where the sgn function is defined by Equation (A.3): sgn[x(n)]={1x(n)��0?1x(n)