A Novel Scheme for Single-Channel Speech Dereverberation

This paper presents a novel scheme for speech dereverberation. The core of our method is a two-stage single-channel speech enhancement scheme. Degraded speech obtains a sparser representation of the linear prediction residual in the first stage of our proposed scheme by applying orthogonal matching pursuit on overcomplete bases, trained by the K-SVD algorithm. Our method includes an estimation of reverberation and mixing time from a recorded hand clap or a simulated room impulse response, which are used to create a time-domain envelope. Late reverberation is suppressed at the second stage by estimating its energy from the previous envelope and removed with spectral subtraction. Further speech enhancement is applied on minimizing the background noise, based on optimal smoothing and minimum statistics. Experimental results indicate favorable quality, compared to two state-of-the-art methods, especially in real reverberant environments with increased reverberation and background noise.

Quartile Method Estimation of Two-Parameter Exponential Distribution Data with Outliers

Several methods have been used to estimate the unknown parameters in the two-parameter exponential distribution. Here we have considered two of these methods, maximum likelihood method and median-first order statistics method. However, in the presence of outliers these methods are not valid. In this paper we propose two approaches that deal with this situation. The idea is based on using first and third quartile instead of the minimum statistics. We investigated the parameters estimate using these methods through simulation study. The new method gives similar results under the normal situation and much better results when the data has outliers.

Discriminative Minimum Statistics Projection Coefficient Feature for Acoustic Context Recognition

Acoustic environment recognition, which can provide the important acoustic context, has been widely used in many applications and is a considerable difficult problem in the real-life and the complex environment. This paper proposes the discriminative minimum statistics project coefficient (MSPC) feature with the information of classification by using partial least squares (PLS). With the minimum statistics (MS) tracked from the input sound, the discriminative MSPC feature is extracted by projecting the MS into lower-dimensional feature subspace learned by using PLS analysis. Based on the proposed discriminative MSPC feature, the acoustic environment recognition is implemented by using Gaussian Mixture Model (GMM) for modeling each sound class. The experimental results show that the proposed discriminative MSPC feature based on PLS outperforms the MSPC feature based on PCA for acoustic environment recognition.