Noise reduction for periodic signals using high-resolution frequency analysis

Toshio Yoshizawa1, Shigeki Hirobayashi1, Tadanobu Misawa1
1Department of Intellectual Information Systems Engineering, Faculty of Technology, University of Toyama, 3190, Gofuku, Toyama-shi, Toyama, Japan

Tóm tắt

Abstract The spectrum subtraction method is one of the most common methods by which to remove noise from a spectrum. Like many noise reduction methods, the spectrum subtraction method uses discrete Fourier transform (DFT) for frequency analysis. There is generally a trade-off between frequency and time resolution in DFT. If the frequency resolution is low, then the noise spectrum can overlap with the signal source spectrum, which makes it difficult to extract the latter signal. Similarly, if the time resolution is low, rapid frequency variations cannot be detected. In order to solve this problem, as a frequency analysis method, we have applied non-harmonic analysis (NHA), which has high accuracy for detached frequency components and is only slightly affected by the frame length. Therefore, we examined the effect of the frequency resolution on noise reduction using NHA rather than DFT as the preprocessing step of the noise reduction process. The accuracy in extracting single sinusoidal waves from a noisy environment was first investigated. The accuracy of NHA was found to be higher than the theoretical upper limit of DFT. The effectiveness of NHA and DFT in extracting music from a noisy environment was then investigated. In this case, NHA was found to be superior to DFT, providing an approximately 2 dB improvement in SNR.

Từ khóa


Tài liệu tham khảo

Boll SF: Suppression of acoustic noise in speech using spectral subtraction. IEEE Trans Acoust Speech, Signal Process ASSP 1979,27(2):113-120. 10.1109/TASSP.1979.1163209

Lin CT: Single-channel speech enhancement in variable noise-level environment. IEEE Trans Syst Man Cybernet A 2003,33(1):137-143.

Kamath SD, Loizou PC: A multi-band spectral subtraction method for enhancing speech corrupted by colored noise. Proceedings of the ICASSP 2002, 4164-4167.

Goh Z, Tan KC, Tan BTG: Postprocessing method for suppressing musical noise generated by spectral subtraction. IEEE Trans Speech Audio Process 1998, 6: 287-292. 10.1109/89.668822

Sorensen K, Andersen S: Speech enhancement with natural sounding residual noise based on connected time-frequency speech presence regions. EURASIP J Appl Signal Process 2005, 18: 2954-2964.

Soon IY, Koh SN: Speech enhancement using 2-D Fourier transform. IEEE Trans Speech Audio Process 2003, 11: 717-724. 10.1109/TSA.2003.816063

Ding H, Soon IY, Koh SN, Yeo CK: A spectral filtering method based on hybrid wiener filters for speech enhancement. Speech Commun 2009, 51: 259-267. 10.1016/j.specom.2008.09.003

Virag N: Single channel speech enhancement based on masking properties of the human auditory system. IEEE Trans Speech Audio Process 1999,7(2):126-137. 10.1109/89.748118

Udrea R, Vizireanu N, Ciochina S: An improved spectral subtraction method for speech enhancement using a perceptual weighting filter. Digital Signal Process 2008,18(4):581-587. 10.1016/j.dsp.2007.08.002

Kauppinen I, Roth K: Improved noise reduction in audio signals using spectral resolution enhancement with time-domain signal extrapolation. IEEE Trans Speech Audio Process 2005, 13: 1210-1216.

Hirobayashi S, Ito F, Yoshizawa T, Yamabuchi T: Estimation of the frequency of non-stationary signals by the steepest descent method. Proceedings of the Fourth Asia-Pacific Conference of Industrial Engineering and Management Systems 2002, 788-791.

George EB, Smith MJT: Analysis-by-synthesis/overlap add sinusoidal modeling applied to the analysis and synthesis of musical tones. J Audio Eng Soc 1992,125(40):497-516.

George EB, Smith MJT: Speech analysis/synthesis and modification using an analysis-by-synthesis/overlap-add sinusoidal model. IEEE Trans Speech Audio Process 1997,5(5):398-406.

Turkey JW, Beaton AE: The fitting of power series, meaning polynomials, illustrated on band-spectroscopic-data. Technometrics 1974, 16: 189-192. 10.2307/1267938

Chambers JM: Computational Methods for Data Analysis. Wiley, New York 1977.

Gill PE, Murray W: Quasi-Newton methods for unconstrained optimization. J Inst Math Appl 1972, 9: 91-108. 10.1093/imamat/9.1.91

Terada T, et al.: Non-stationary waveform analysis and synthesis using generalized harmonic analysis. IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis 1994, 429-432.

Wiener N: The Fourier Integral and Certain of Its Applications. Dover Publications, Inc., New York; 1958:158-199.

Muraoka T, Kiriu S, Kamiya Y: Fast algorithm for generalized harmonic analysis (GHA). The 47th IEEE International Midwest Symposium on Circuit and Systems 2004, 153-156.

Hirata Y: Non-harmonic Fourier analysis available for detecting very low-frequency components. J Sound Vib 2005,287(3):611-613.

Kauppinen I, Roth K: An adaptive technique for modeling audio signals. In Proceedings of the 4th International Conference on Digital Audio Effects (DAFx-01). Limerick, Ireland; 2001:1-4.

Kauppinen I, Roth K: Audio signal extrapolation--theory and applications. In Proceedings of the 5th International Conference on Digital Audio Effects (DAFx-02). Hamburg, Germany; 2002:105-110.

Berouti M, Schwartz R, Makhoul J: Enhancement of speech corrupted by acoustic noise. Proc IEEE ICASSP'79 1979, 208-211.