Speech Sources In Reverberant Environments Research Articles

Separation of Multiple Speech Sources in Reverberant Environments Based on Sparse Component Enhancement

Separation of Multiple Speech Sources in Reverberant Environments Based on Sparse Component Enhancement

Multi-Source DOA Estimation in Reverberant Environments by Jointing Detection and Modeling of Time-Frequency Points

In this article, the direction of arrival (DOA) estimation of multiple speech sources in reverberant environments is investigated based on the recording of a soundfield microphone. First, the recordings are analyzed in the time-frequency (T-F) domain to detect both “points” (single T-F points) and “regions” (multiple, adjacent T-F points) corresponding to a single source with low reverberation (known as low-reverberant-single-source (LRSS) points). Then, a LRSS point detection algorithm is proposed based on a joint dominance measure and instantaneous single-source point (SSP) identification. Following this, initial DOA estimates obtained for the detected LRSS points are analyzed using a Gaussian Mixture Model (GMM) derived by the Expectation-Maximization (EM) algorithm to cluster components into sources or outliers using a rule-based method. Finally, the DOA of each actual source is obtained from the estimated source components. Experiments on both simulated data and data recorded in an actual acoustic chamber demonstrate that the proposed algorithm exhibits improved performance for the DOA estimation in reverberant environments when compared to several existing approaches.

Solving permutations in frequency-domain for blind separation of an arbitrary number of speech sources

Blind separation of speech sources in reverberant environments is usually performed in the time-frequency domain, which gives rise to the permutation problem: the different ordering of estimated sources for different frequency components. A two-stage method to solve permutations with an arbitrary number of sources is proposed. The suggested procedure is based on the spectral consistency of the sources. At the first stage frequency bins are compared with each other, while at the second stage the neighboring frequencies are emphasized. Experiments for perfect separation situations and for live recordings show that the proposed method improves the results of existing approaches.

Multiple Reverberant Sound Localization Based on Rigorous Zero-Crossing-Based ITD Selection

This letter presents a multiple sound localization method for non-stationary speech sources in reverberant environments. Although zero-crossing-based interaural time differences (ITDs) were robust to diffuse noise and energy-based onset detection tackled reverberant speech , the onset detection method was sensitive to parameters. The proposed method employs echo-free onset detection , and this letter adds reverberation time estimation to obtain accurate echoes and signal-to-noise ratio estimation to assist the onset detection based on typical properties of acoustic reverberation in selecting ITDs corresponding to source locations. Experimental results indicate the effectiveness of the proposed method in reverberant environments.

Relative Transfer Function Identification Using Convolutive Transfer Function Approximation

In this paper, we present a relative transfer function (RTF) identification method for speech sources in reverberant environments. The proposed method is based on the convolutive transfer function (CTF) approximation, which enables to represent a linear convolution in the time domain as a linear convolution in the short-time Fourier transform (STFT) domain. Unlike the restrictive and commonly used multiplicative transfer function (MTF) approximation, which becomes more accurate when the length of a time frame increases relative to the length of the impulse response, the CTF approximation enables representation of long impulse responses using short time frames. We develop an unbiased RTF estimator that exploits the nonstationarity and presence probability of the speech signal and derive an analytic expression for the estimator variance. Experimental results show that the proposed method is advantageous compared to common RTF identification methods in various acoustic environments, especially when identifying long RTFs typical to real rooms.

Sound localization in reverberant environment based on the model of the precedence effect

This paper presents a model-based method for sound localization of concurrent and continuous speech sources in reverberant environment. A new algorithm adopted from the echo-avoidance model of the precedence effect was used to detect the echo-free onsets by specifying a generalized pattern of impulse response. Fine structure time differences were calculated from the zero-crossing points in different microphones. They were integrated into an azimuth histogram by the restrictions between them. Two sound sources were localized in both an anechoic chamber and a normal room which has walls, floor, and ceiling made of concrete. The time segment needed for localization was 0.5-2 s and the accuracy was a few degrees in both environments.