This paper proposes a direction-of-arrival (DOA) estimation method of multiple speech sources from a stereophonic mixture in an underdetermined case where the number of sources exceeds the number of sensors. The method relies on the sparseness of speech signals in time-frequency (T-F) domain representation which means multiple independent speakers have a small overlap. At first, a selection of T-F cells bearing reliable spatial information is proposed by an introduced reliability index which is defined by the estimated interaural phase difference at each T-F cell. Then, a statistical error propagation model between the phase difference at T-F cell and its consequent DOA is introduced. By employing this model and the sparseness in T-F domain the DOA estimation problem is altered to obtaining local peaks of probability density function of DOA. Finally the kernel density estimator approach based on the proposed statistical model is applied. The performance of the proposed method is assessed by conducted experiments. Our method outperforms others both in accuracy for real observed data and in robustness for simulation with additional diffused noise.

In this letter, a combined method based on the fractional linear and the fractional bilinear time-frequency representations (TFRs) is proposed. The method combines the windowed fractional short-time Fourier transform with the fractional Wigner distribution (WD) to estimate the instantaneous frequency (IF) of signals in the appropriate fractional time-frequency domain. For a multi-component signal, the method can significantly eliminate the cross terms and improve the time-frequency resolution of the auto-terms. It is applied to the detection and parameter estimation of linear frequency modulated (LFM) signals. The computer simulations clearly demonstrate that the method is effective.

The constant-Q based wavelet transform is the most effective means of quantitatively characterizing high frequency transient signals. This study develops a novel non constant-Q based multi-resolution transform (NCQM) and provides a precision analysis descriptor for both low and high frequency transients. The properties of this novel NCQM kernel are thoroughly examined and then the striking conceptual resemblance, energy conservation characteristic, and power spectrum close forms are derived. The rapid algorithm of NCQM is also presented and its excellent performance in noisy environments is demonstrated.

A simple but efficient method to improve readability of discrete pseudo time-frequency representations (TFRs) of nonstationary signals by the reassignment of the representations in discrete frequency dimension is presented. The method does not rely on the nonzero time derivative of the window function employed in the estimation of pseudo TFR. This property of the reassignment method is advantageous because the method can provide an improved readability in the situation when a known reassignment method is unefficient. The reassignment of the TFRs of corrupted signals is discussed. Numerical examples are included to illustrate the performance of the proposed method.