Sperm whales (Physeter macrocephalus) emit acoustic signals when diving to search for food. Because they dive to depths of over 1500 m, conventional localization methods are impractical. Sperm whales produce a series of loud impulsive echolocation sounds called `clicks' at a fairly constant rate, and these signals can be used for localization. A geometric approach is considered using signals from the direct and surface reflection paths. The time difference of arrival (TDOA) between these paths is used for localization. Real sperm whale data from the Atlantic Undersea Test Center (AUTC) is used to evaluate the proposed method.

Reverberation is a major problem for time delay estimation (TDE) in enclosed environments. In this paper, a robust TDE method based on the generalized cross-correlation (GCC) is proposed. An efficient preprocessing technique to calculate the all-pass component is introduced to improve the performance of GCC-based techniques in reverberant conditions. Performance results are given which demonstrate that the proposed approach provides better performance for a wide range of microphone locations and reverberation times. Results in real acoustic environments confirm the effectiveness of the proposed TDE method. Compared to other TDE methods, our solution has low computational complexity and can be employed in real-time applications.

This paper presents a single-microphone approach to the enhancement of noisy reverberant speech via inverse filtering and spectral processing. An efficient algorithm is used to blindly estimate the inverse filter of the Room Impulse Response (RIR). This filter is used to attenuate the early reverberation. A simple technique to blindly determine the filter length is presented. A two-step spectral subtraction method is proposed to efficiently reduce the effects of background noise and the residual reverberation on the equalized impulse response. In general, the equalized impulse response has two detrimental effects, late impulses and pre-echoes. For the late impulses, an efficient spectral subtraction algorithm is developed which introduces only minor musical noise. Then a new algorithm is introduced which reduces the remaining pre-echo effects. The performance of this two-stage method is examined in different reverberant conditions including real environments. It is also evaluated with white Gaussian and recorded babble noise. The results obtained demonstrate that the proposed blind method is superior in terms of reducing early and late reverberation effects and noise compared to well known single-microphone techniques in the literature.

In this paper, we propose a speech-model based method using the linear predictive (LP) residual of the speech signal and the maximum-likelihood (ML) estimator proposed in “Blind estimation of reverberation time,” (R. Ratnam , J. Acoust. Soc. Amer., 2004) to blindly estimate the reverberation time (RT60). The input speech is passed through a low order linear predictive coding (LPC) filter to obtain the LP residual signal. It is proven that the unbiased autocorrelation function of this LP residual has the required properties to be used as an input to the ML estimator. It is shown that this method can successfully estimate the reverberation time with less data than existing blind methods. Experiments show that the proposed method can produce better estimates of RT60, even in highly reverberant rooms. This is because the entire input speech data is used in the estimation process. The proposed method is not sensitive to the type of input data (voiced, unvoiced), number of gaps, or window length. In addition, evaluation using white Gaussian noise and recorded babble noise shows that it can estimate RT60in the presence of (moderate) background noise.

This paper presents a method based on higher order statistics (HOS), namely the normalized third-order moment (skewness), for blind estimation of the inverse filter of the room impulse response (RIR). Skewness is used as a measure of asymmetry, and a comprehensive comparison with the commonly used metric (kurtosis) is presented. It is shown that a sufficiently long linear predictive (LP) residual of the speech signal has an asymmetric pdf with sufficient skewness to be used as a score function for the HOS-based approach. The proposed algorithm is optimized for the inverse filter estimation problem. This optimization includes an efficient initialization for high reverberation intensities, enabling the method to be employed in highly reverberant rooms. The direct-to-reverberation ratio (DRR) as well as the equalized impulse response clearly show that our method can estimate the inverse filter even in highly reverberant environments. In addition, performance results using recorded background noise and in time-varying environments illustrate that our approach is applicable in real world situations. The proposed method is shown to be superior to the method by Wu and Wang, particularly in terms of reducing the coloration effect. Experiments under different acoustic conditions confirm the effectiveness of the proposed method for time delay estimation (TDE). Finally, the proposed algorithm is used as the first-stage of monaural segregation, and it is shown to improve the performance under different conditions.