Browse > Article

Speech Enhancement Based on Modified IMCRA Using Spectral Minima Tracking with Weighted Subband Selection  

Park, Yun-Sik (Department of Electronic Engineering, Inha University)
Park, Gyu-Seok (Department of Electronic Engineering, Inha University)
Lee, Sang-Min (Department of Electronic Engineering, Inha University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.49, no.3, 2012 , pp. 89-97 More about this Journal
Abstract
In this paper, we propose a novel approach to noise power estimation for speech enhancement in noisy environments. The method based on IMCRA (improved minima controlled recursive averaging) which is widely used in speech enhancement utilizes a rough VAD (voice activity detection) algorithm which excludes speech components during speech periods in order to improves the performance of the noise power estimation by reducing the speech distortion caused by the conventional algorithm based on the minimum power spectrum derived from the noisy speech. However, since the VAD algorithm is not sufficient to distinguish speech from noise at non-stationary noise and low SNRs (signal-to-noise ratios), the speech distortion resulted from the minimum tracking during speech periods still remained. In the proposed method, minimum power estimate obtained by IMCRA is modified by SMT (spectral minima tracking) to reduce the speech distortion derived from the bias of the estimated minimum power. In addition, in order to effectively estimate minimum power by considering the distribution characteristic of the speech and noise spectrum, the presented method combines the minimum estimates provided by IMCRA and SMT depending on the weighting factor based on the subband. Performance of the proposed algorithm is evaluated by subjective and objective quality tests under various environments and better results compared with the conventional method are obtained.
Keywords
음성 향상;잡음전력 추정;최소값 추적;
Citations & Related Records
연도 인용수 순위
  • Reference
1 G. Doblinger, "Computationally efficient speech enhancement by spectral minima tracking in subbands," in Proc. EUROSPEECH, vol. 2, pp. 1513-1516, 1995.
2 R. Martin, "Spectral subtraction based on minimum statistics," in Proc. Eur. Signal Processing Conf., pp. 1182-1185, 1994.
3 R. Martin, "Noise power spectral density estimation based. on optimal smoothing and minimum statistics," IEEE. Trans. on Speech and Audio Processing, vol. 9, no. 5, pp. 504-512, July 2001.   DOI   ScienceOn
4 I. Cohen, B. Berdugo, "Noise estimation by minima controlled recursive averaging for robust speech enhancement," IEEE Signal Processing Letters, vol. 9, no. 1, pp. 12-15, Jan. 2002.   DOI
5 I. Cohen, "Noise spectrum estimation in adverse environments: Improved minima controller recursive averaging," IEEE Trans. Speech Audio Processing, vol. 11, no. 5, pp. 466-475, Sep. 2003.   DOI   ScienceOn
6 Y. -S. Park and J. -H. Chang, "A novel approach to a robust a priori SNR estimator in speech enhancement," IEICE Trans. on Communications, vol. E90-B, no.8, pp 2182-2185 Aug. 2007.   DOI   ScienceOn
7 TIA/EIA/IS-127, Enhanced variable rate codec, speech service option 3 for wideband spread spectrum digital systems, 1996.
8 G. D. Wu and C. T. Lin, "Word boundary detection with mel-scale frequency bank in noise environment," IEEE Trans. Speech Audio Process., vol. 8, no. 3, pp. 541-554, May 2000.   DOI
9 Y. Tian, J. Wu, Z. Wang, and D. Lu, "Robust noisy speech recognition with adaptive frequency bank selection," in Proc. ICMI, pp.75-80, 2002.
10 B.F. Wu, K.C. Wang, "Robust endpoint detection algorithm based on the adaptive band-partitioning spectral entropy in adverse environments," IEEE Trans. Speech Audio Process. vol. 13, no. 5, pp. 762-775, Sept. 2005.
11 Yi Hu and P. C. Loizou, "Evaluation of objective quality measures for speech enhancement," IEEE Trans. ASLP, vol. 16, pp. 229-238, Jan. 2008.