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Keywords
(7)
Acoustic Noise
Communication System
High Speed
Noise Suppression
Spectral Subtraction
Speech Analysis
Speech Recognition
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Suppression of acoustic noise in speech using spectral subtraction
Suppression of acoustic noise in speech using spectral subtraction,10.1109/TASSP.1979.1163209,IEEE Transactions on Acoustics, Speech, and Signal Proce
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Suppression of acoustic noise in speech using spectral subtraction
(
Citations: 1262
)
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S. Boll
A stand-alone
noise suppression
algorithm is presented for reducing the spectral effects of acoustically added noise in speech. Effective performance of digital speech processors operating in practical environments may require suppression of noise from the digital wave-form.
Spectral subtraction
offers a computationally efficient, processor-independent approach to effective digital speech analysis. The method, requiring about the same computation as high-speed convolution, suppresses stationary noise from speech by subtracting the spectral noise bias calculated during nonspeech activity. Secondary procedures are then applied to attenuate the residual noise left after subtraction. Since the algorithm resynthesizes a speech waveform, it can be used as a pre-processor to narrow-band voice communications systems,
speech recognition
systems, or speaker authentication systems.
Journal:
IEEE Transactions on Acoustics, Speech, and Signal Processing
, vol. 27, no. 2, pp. 113-120, 1979
DOI:
10.1109/TASSP.1979.1163209
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Citation Context
(726)
...Several denoising methods and algorithms are proposed for the removal of noise (Boll
1979
; Ephraim
1992
; Ephraim and Trees
1995
; Sameti et al...
B. Mohan Kumar
,
et al.
Optimal FPGA implementation of CL multiwavelets architecture for signa...
...A typical technique is spectral subtraction (Boll,
1979
)...
Ryu Takeda
,
et al.
Efficient Blind Dereverberation and Echo Cancellation Based on Indepen...
...The spectral subtraction-based noise suppression system was proposed by Boll (
1979
)...
Harjit Pal Singh
,
et al.
Evaluating the perceived voice quality on VoIP network using interpola...
...An estimate of the speech spectrum can be derived by subtracting the estimated noise spectrum from the corrupted signal spectrum . The most frequently used technique to accomplish this is to perform spectral subtraction [
42
] by applying an SNR-dependent gain function in the frequency domain...
Tobias May
,
et al.
Noise-Robust Speaker Recognition Combining Missing Data Techniques and...
...For example, spectral subtraction (SS) [
14
] is the most popular and simplest method of nonlinear signal processing and enables efficient noise reduction, but it always suffers from the problem of a large amount of musical noise...
Hiroshi Saruwatari
,
et al.
Musical Noise Controllable Algorithm of Channelwise Spectral Subtracti...
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Citations
(1262)
Optimal FPGA implementation of CL multiwavelets architecture for signal denoising application
B. Mohan Kumar
,
R. Vidhya Lavanya
,
E. P. Sumesh
Journal:
International Journal of Electronics - INT J ELECTRON
, vol. ahead-of-p, no. ahead-of-p, pp. 1-14, 2012
Efficient Blind Dereverberation and Echo Cancellation Based on Independent Component Analysis for Actual Acoustic Signals
Ryu Takeda
,
Kazuhiro Nakadai
,
Toru Takahashi
,
Kazunori Komatani
,
Tetsuya Ogata
,
Hiroshi G. Okuno
Journal:
Neural Computation - NECO
, vol. 24, no. 1, pp. 234-272, 2012
Evaluating the perceived voice quality on VoIP network using interpolated FIR filter algorithm
Harjit Pal Singh
,
Sarabjeet Singh
,
R. K. Sarin
,
Jasvir Singh
Journal:
International Journal of Electronics - INT J ELECTRON
, vol. ahead-of-p, no. ahead-of-p, pp. 1-21, 2012
Noise-Robust Speaker Recognition Combining Missing Data Techniques and Universal Background Modeling
Tobias May
,
Steven van de Par
,
Armin Kohlrausch
Journal:
IEEE Transactions on Audio, Speech & Language Processing - TASLP
, vol. 20, no. 1, pp. 108-121, 2012
A neural network and multiple regression method for the characterization of the depth of weld penetration in laser welding based on acoustic signatures
(
Citations: 3
)
Wei Huang
,
Radovan Kovacevic
Journal:
Journal of Intelligent Manufacturing - J INTELL MANUF
, vol. 22, no. 2, pp. 131-143, 2011