Yun-Sik Park

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a r t i c l e i n f o a b s t r a c t Keywords: Speech enhancement Minima controlled recursive averaging (MCRA) Conditional maximum a posteriori (CMAP) 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(More)
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)(More)
—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(More)
—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(More)
In this paper, we apply a discriminative weight training to a statistical model-based voice activity detection (VAD). In our approach, the VAD decision rule is expressed as the geometric mean of optimally weighted likelihood ratios (LRs) based on a minimum classification error (MCE) method. That approach is different from that of previous works in that(More)
SUMMARY 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(More)
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.(More)
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(More)