Patent TW-201714170-A (TW)
Title: Underwater audio signal time-frequency energy distribution characteristic analysis method based on empirical mode decomposition characterized in that the adaptive decomposition and analysis of underwater audio signal allows us to better understand the temporal frequency energy distribution characteristics of the underwater audio signal
Inventor: LIN JIN-FENG
IPC: G10L25/27
Filed: 2015-10-05
Abstract: The present invention relates to an underwater audio signal time-frequency energy distribution characteristic analysis method based on empirical mode decomposition, and particularly, to an underwater audio signal time-frequency energy distribution characteristic analysis method developed by means of integration of empirical mode decomposition. The rationale of integration of empirical mode decomposition lies in that the adaptive decomposition and analysis of underwater audio signal enables better understanding of the temporal frequency energy distribution characteristics of the underwater audio. The method comprises the following steps: using an empirical mode decomposition mechanism to decompose the underwater audio signal into several intrinsic mode functions of underwater audio signal and a residual function of underwater audio signal; defining intrinsic mode functions as a primary intrinsic mode function residual cluster, if these intrinsic mode functions have a distribution ratio of the intrinsic mode function energy with respect to the total energy being greater than 10%; defining the residual function as a residual function cluster, if the residual function has a distribution ratio of the residual function energy with respect to the total energy distribution being greater than 10%; characterizing the distribution ratio of the residual function cluster energy and the primary intrinsic mode functions with respect to the total energy; characterizing the standard deviation of the average instantaneous frequency and instantaneous frequency for the primary intrinsic mode function cluster and the residual function cluster; analyzing the distribution ratio of the marginal frequency energy with respect to the total energy for the primary intrinsic mode function cluster and the residual function cluster; defining a marginal frequency cluster for the primary intrinsic mode function and residual function cluster and the residual function cluster if it has a distribution ratio of the marginal frequency energy with respect to the total energy being greater than 5%; characterizing the distribution ratio of the marginal frequency cluster energy with respect to the total energy for the primary time intrinsic mode function cluster and the residual function cluster; using two seconds of the underwater human speech audio signal as an analysis time window to analyze, for the primary time intrinsic mode function cluster and the residual function cluster, the distribution ratio of Hilbert spectrum energy with respect to the total energy over the two-second time window; if the distribution ratio of Hilbert spectrum energy with respect to the total energy over the two-second time window is greater than 1%, it is defined as the Hilbert spectrum cluster of the primary time intrinsic mode functions and residual function, and the distribution ratio of Hilbert spectrum cluster energy of the primary time intrinsic mode functions and the residual function is characterized.