Noise Estimation for Speech Enhancement in Non-Stationary Environments-A New Method
This paper presents a new method for estimating the nonstationary
noise power spectral density given a noisy signal. The
method is based on averaging the noisy speech power spectrum using
time and frequency dependent smoothing factors. These factors are
adjusted based on signal-presence probability in individual frequency
bins. Signal presence is determined by computing the ratio of the
noisy speech power spectrum to its local minimum, which is updated
continuously by averaging past values of the noisy speech power
spectra with a look-ahead factor. This method adapts very quickly to
highly non-stationary noise environments. The proposed method
achieves significant improvements over a system that uses voice
activity detector (VAD) in noise estimation.
[1] Sohn. J, Kim. N, "Statistical model-based voice activity detection",
IEEE Signal Process. Lett. 6(1), pp. 1-3, 1999.
[2] Malah.D, Cox.R, Accardi.A, "Tracking speech-presence uncertainty to
improve speech enhancement in non-stationary environments", Proc.
IEEE Internat. On Conf. Acoust. Speech Signal Process., pp. 789-792,
1999.
[3] Martin.R, "Noise power spectral density estimation based on optimal
smoothing and minimum statistics", IEEE Tran. Speech Audio Process.,
9(5), pp. 504-512,2001.
[4] Cohen.I, "Noise estimation by minima controlled recursive averaging for
robust speech enhancement", IEEE Signal Process. Lett., 9(1), pp. 12-15,
2002.
[5] Cohen.I., "Noise spectrum estimation in adverse environments: improved
minima controlled recursive averaging", IEEE Trans. Speech Audio
Process., 11(5), pp. 466-475, 2003.
[6] Doblinger.G, "Computationally efficient speech enhancement by spectral
minima tracking in subbands", Proc. Eurospeech, pp.1513-1516, 1995.
[1] Sohn. J, Kim. N, "Statistical model-based voice activity detection",
IEEE Signal Process. Lett. 6(1), pp. 1-3, 1999.
[2] Malah.D, Cox.R, Accardi.A, "Tracking speech-presence uncertainty to
improve speech enhancement in non-stationary environments", Proc.
IEEE Internat. On Conf. Acoust. Speech Signal Process., pp. 789-792,
1999.
[3] Martin.R, "Noise power spectral density estimation based on optimal
smoothing and minimum statistics", IEEE Tran. Speech Audio Process.,
9(5), pp. 504-512,2001.
[4] Cohen.I, "Noise estimation by minima controlled recursive averaging for
robust speech enhancement", IEEE Signal Process. Lett., 9(1), pp. 12-15,
2002.
[5] Cohen.I., "Noise spectrum estimation in adverse environments: improved
minima controlled recursive averaging", IEEE Trans. Speech Audio
Process., 11(5), pp. 466-475, 2003.
[6] Doblinger.G, "Computationally efficient speech enhancement by spectral
minima tracking in subbands", Proc. Eurospeech, pp.1513-1516, 1995.
@article{"International Journal of Electrical, Electronic and Communication Sciences:61816", author = "Ch.V.Rama Rao and Gowthami. and Harsha. and Rajkumar. and M.B.Rama Murthy and K.Srinivasa Rao and K.AnithaSheela", title = "Noise Estimation for Speech Enhancement in Non-Stationary Environments-A New Method", abstract = "This paper presents a new method for estimating the nonstationary
noise power spectral density given a noisy signal. The
method is based on averaging the noisy speech power spectrum using
time and frequency dependent smoothing factors. These factors are
adjusted based on signal-presence probability in individual frequency
bins. Signal presence is determined by computing the ratio of the
noisy speech power spectrum to its local minimum, which is updated
continuously by averaging past values of the noisy speech power
spectra with a look-ahead factor. This method adapts very quickly to
highly non-stationary noise environments. The proposed method
achieves significant improvements over a system that uses voice
activity detector (VAD) in noise estimation.", keywords = "Noise estimation, Non-stationary noise, Speechenhancement.", volume = "4", number = "10", pages = "1543-4", }