Yun-Sik Park

Frequency Domain Acoustic Echo Suppression Based on Soft Decision

In this letter, we propose a novel acoustic echo suppression (AES) technique based on soft decision in a frequency domain. The proposed approach provides an efficient and unified framework for such procedures as AES gain computation, AES gain modification using soft decision, and estimation of relevant parameters based on the same statistical model assumption of the near-end and far-end signal instead of the conventional strategies requiring the additional residual echo suppression (RES) step. Performances of the proposed AES algorithm are evaluated by objective and subjective tests under various environments, and better results compared with the conventional AES method are obtained.

A Novel Approach to a Robust a Priori SNR Estimator in Speech Enhancement

SUMMARY This paper presents a novel approach to single channel speech enhancement in noisy environments. Widely adopted noise reduction techniques based on the spectral subtraction are generally expressed as a spectral gain depending on the signal-to-noise ratio (SNR) [1]–[4]. As the estimation method of the SNR, the well-known decision-directed (DD) estimator of Ephraim and Malah efficiently is known to reduces musical noise in noise frames, but the ap rioriSNR, which is a crucial parameter of the spectral gain, follows the a posteriori SNR with a delay of one frame in speech frames [5]. Therefore, the noise suppression gain using the delayed ap rioriSNR, which is estimated by the DD algorithm matches the previous frame rather than the current one, so after noise suppression, this degrades the performance of a noise reduction during abrupt transient parts. To overcome this artifact, we propose a computationally simple but effective speech enhancement technique based on the sigmoid type function to adaptively determine the weighting factor of the DD algorithm. Actually, the proposed approach avoids the delay problem of the ap rioriSNR while maintaining the advantage of the DD algorithm. The performance of the proposed enhancement algorithm is evaluated by the objective and subjective test under various environments and yields better results compared with the conventional DD scheme based approach.

A Probabilistic Combination Method of Minimum Statistics and Soft Decision for Robust Noise Power Estimation in Speech Enhancement

In this letter, we propose a novel approach to noise power estimation for robust speech enhancement in noisy environments. From investigation of the state-of-the-art techniques for noise power estimation, it is discovered that the previously known methods are accurate mostly either during speech absence or speech presence, but none of it works well in both situations. Our approach combines minimum statistics (MS) and soft decision (SD) techniques based on probability of speech absence. The performance of the proposed approach is evaluated by a quantitative comparison method and subjective test under various noise environments and found to yield better results compared with conventional MS- and SD-based schemes.

Speech enhancement based on minima controlled recursive averaging incorporating conditional maximum a posteriori criterion

In this paper, we propose a novel approach to improve the performance of minima controlled recursive averaging (MCRA) based on a conditional maximum a posteriori (MAP) criterion. From an investigation of the MCRA scheme, it is discovered that MCRA method cannot take full consideration of the inter-frame correlation of voice activity since the noise power estimate is adjusted by the speech presence probability depending on an observation of the current frame. To avoid this phenomenon, the proposed MCRA approach incorporates the conditional MAP criterion in which the noise power estimate is obtained using the speech presence probability conditioned on both the current observation and the speech activity decision in the previous frame Experimental results show that the proposed MCRA technique based on conditional MAP yields better results compared to the conventional MCRA method.

Integrated acoustic echo and background noise suppression technique based on soft decision

In this paper, we propose an efficient integrated acoustic echo and noise suppression algorithm using the combined power of acoustic echo and background noise within a soft decision framework. The combined power of the acoustic echo and noise is adopted to the integrated suppression algorithm based on soft decision to address the artifacts such as the nonlinear distortion and the disturbed noise introduced from the conventional methods. Specifically, in the unified frequency domain architecture, the acoustic echo and noise signal are efficiently able to be suppressed through the acoustic echo suppression algorithm based on soft decision without the help of the additional noise reduction technique.

Improved minima controlled recursive averaging technique using conditional maximum a posteriori criterion for speech enhancement

In this paper, we propose a novel approach to improve the performance of minima controlled recursive averaging (MCRA) based on a conditional maximum a posteriori (MAP) criterion. From an investigation of the MCRA scheme, it is discovered that the MCRA method cannot take full consideration of the inter-frame correlation of voice activity since the noise power estimate is adjusted by the speech presence probability depending on an observation of the current frame. To avoid this phenomenon, the proposed MCRA approach incorporates the conditional MAP criterion in which the noise power estimate is obtained using the speech presence probability conditioned on both the current observation and the speech activity decision in the previous frame. Experimental results show that compared to the conventional MCRA method the proposed MCRA technique based on conditional MAP obtains low estimation error and when integrated into a speech enhancement system achieves improved speech quality.

A Support Vector Machine-Based Voice Activity Detection Employing Effective Feature Vectors

In this letter, we propose effective feature vectors to improve the performance of voice activity detection (VAD) employing a support vector machine (SVM), which is known to incorporate an optimized nonlinear decision over two different classes. To extract the effective feature vectors, we present a novel scheme that combines the a posteriori SNR, a priori SNR, and predicted SNR, widely adopted in conventional statistical model-based VAD.

An Improved Speech Absence Probability Estimation based on Environmental Noise Classification

In this paper, we propose a improved speech absence probability estimation algorithm by applying environmental noise classification for speech enhancement. The previous speech absence probability required to seek a priori probability of speech absence was derived by applying microphone input signal and the noise signal based on the estimated value of a posteriori SNR threshold. In this paper, the proposed algorithm estimates the speech absence probability using noise classification algorithm which is based on Gaussian mixture model in order to apply the optimal parameter each noise types, unlike the conventional fixed threshold and smoothing parameter. Performance of the proposed enhancement algorithm is evaluated by ITU-T P.862 PESQ (perceptual evaluation of speech quality) and composite measure under various noise environments. It is verified that the proposed algorithm yields better results compared to the conventional speech absence probability estimation algorithm.

A statistical model-based double-talk detection incorporating soft decision

In this paper, we propose a novel double-talk detection (DTD) technique based on a soft decision in the frequency domain. The proposed method provides an efficient procedure to detect the double-talk situation by the use of the global near-end speech presence probability (GNSPP) and voice activity detection (VAD) of the near-end and far-end signal. Specifically, the GNSPP is derived based on a statistical method of speech and is employed to determine the double-talk presence in a given frame. The performance of our approach is evaluated by objective tests under different environments, and it is found that the suggested method yields better results compared with the conventional scheme.

Fast communication: Double-talk detection based on soft decision for acoustic echo suppression

In this paper, we propose a novel double-talk detection (DTD) technique based on soft decision in the frequency domain. In the proposed method, global near-end speech presence probability (GNSPP) considering the statistical model assumption and voice activity detection (VAD) decision of the near-end and far-end signal are applied to the DTD algorithm in the frequency domain instead of the traditional hard decision scheme using cross-correlation coefficients. The performance of the proposed algorithm is evaluated by the objective test under various environments, and yields better results compared with the conventional scheme.