An approach for time-evolving sound speed profiles tracking in shallow water is discussed. The inversion of time-evolving sound speed profiles is modeled as a state-space estimation problem, which includes a state equ...An approach for time-evolving sound speed profiles tracking in shallow water is discussed. The inversion of time-evolving sound speed profiles is modeled as a state-space estimation problem, which includes a state equation for predicting the time-evolving sound speed profile and a measurement equation for incorporating local acoustic measurements. In the paper, auto-regression （AR） method is introduced to obtain a high-order AR evolution model of the sound speed field time variations, and the ensemble Kalman filter is utilized to track the sound speed field. To validate the approach, the accuracy in sound speed estimation is analyzed via a numerical implementation using the ASIAEX experimental environment and the sound velocity measurement data. Compared with traditional approaches based on the state evolution represented as a random walk, simulation results show the proposed AR method can effectively reduce the tracking errors of sound speed, and still keep good tracking performance at low signal-to-noise ratios.展开更多
分析语音信号声道特征参数提取问题,针对自相关法的缺陷,提出声道特征参数提取的改进算法。介绍其运算步骤和流程,考虑FPGA适于短期开发及高速性的优点,设计Finite State Machine来控制复杂运算操作及对寄存器的频繁访问。利用Cyclone E...分析语音信号声道特征参数提取问题,针对自相关法的缺陷,提出声道特征参数提取的改进算法。介绍其运算步骤和流程,考虑FPGA适于短期开发及高速性的优点,设计Finite State Machine来控制复杂运算操作及对寄存器的频繁访问。利用Cyclone EP1C6 FPGA实现语音信号声道特征参数提取算法。展开更多
基金supported by the National Natural Science Foundation of China(41576103)
文摘An approach for time-evolving sound speed profiles tracking in shallow water is discussed. The inversion of time-evolving sound speed profiles is modeled as a state-space estimation problem, which includes a state equation for predicting the time-evolving sound speed profile and a measurement equation for incorporating local acoustic measurements. In the paper, auto-regression （AR） method is introduced to obtain a high-order AR evolution model of the sound speed field time variations, and the ensemble Kalman filter is utilized to track the sound speed field. To validate the approach, the accuracy in sound speed estimation is analyzed via a numerical implementation using the ASIAEX experimental environment and the sound velocity measurement data. Compared with traditional approaches based on the state evolution represented as a random walk, simulation results show the proposed AR method can effectively reduce the tracking errors of sound speed, and still keep good tracking performance at low signal-to-noise ratios.
文摘分析语音信号声道特征参数提取问题,针对自相关法的缺陷,提出声道特征参数提取的改进算法。介绍其运算步骤和流程,考虑FPGA适于短期开发及高速性的优点,设计Finite State Machine来控制复杂运算操作及对寄存器的频繁访问。利用Cyclone EP1C6 FPGA实现语音信号声道特征参数提取算法。
