Noise suppression system, method and program

Patent metadata

Field	Value
Publication number	US-9613631-B2
Application number	US-48959406-A
Country	US
Kind code	B2
Filing date	Jul 20, 2006
Priority date	Jul 27, 2005
Publication date	Apr 4, 2017
Grant date	Apr 4, 2017

Abstract

Disclosed is a noise suppression system including a unit for calculating a noise mean spectrum from an input signal, a unit for deriving the provisional estimate speech from the input signal and the noise mean spectrum, a reference speech pattern, and a unit for correcting the provisional estimate speech using the reference pattern.

First claim

What is claimed is:

1. A noise suppression system, comprising:
a unit, as executed by a processor, for successively acquiring an input signal in a spectrum domain;
a unit, as executed by said processor, for successively estimating an instant noise value in the spectrum domain from said input signal;
a unit, as executed by said processor, for deriving a provisional estimate speech in the spectral domain from said input signal and said instant noise value; and
a unit, as executed by said processor, for correcting said provisional estimate speech using a reference pattern of speech stored in a storage unit, said correcting using a distribution for said reference pattern as comprising clean speech without a noise contamination,
wherein, in said unit for deriving said provisional estimate speech, said provisional estimate speech is derived by suppressing a noise element in said input signal with said instant noise value, and
wherein said unit for correcting said provisional estimate speech includes:
a unit for transforming said provisional estimate speech derived in the spectral domain into a feature vector in a logarithmic domain or a cepstrum domain;
a unit for correcting said provisional estimate speech, transformed into said feature vector, using a reference pattern in a feature vector domain;
a unit for transforming said corrected provisional estimate speech in the spectrum domain; and
a unit for acquiring an estimate speech by second suppressing, in the spectrum domain, a noise element in said input signal.

2. The noise suppression system according to claim 1 , wherein said unit for correcting said provisional estimate speech presupposes a probability distribution as said reference pattern and derives an expected value of speech from a probability that the probability distribution forming said reference pattern outputs the provisional estimate speech and from a mean value of the probability distribution forming said reference pattern, said expected value of speech being used as a value for correction of the provisional estimate speech.

3. The noise suppression system according to claim 1 , wherein said unit for correcting said provisional estimate speech corrects the provisional estimate speech, using a reference pattern including a plurality of speech patterns, and
wherein a reference pattern which is closest to an input speech is selected and used as a value for a correction of the provisional estimate speech, or a plurality of speech patterns constituting said reference pattern, closer to said input speech, are averaged with weights which are dependent on distances between the provisional estimate speech and the respective speech patterns.

4. The noise suppression system according to claim 1 , wherein said unit for correcting said provisional estimate speech finds a standard deviation of noise and takes into account said standard deviation of noise to control said correction of said provisional estimate speech.

5. The noise suppression system according to claim 4 , further comprising a unit for calculating said provisional estimate speech and a reliability of said provisional estimate speech from said standard deviation of noise, a value of said provisional estimate speech and the reliability of said provisional estimate speech both being taken into account for performing said correction of said provisional estimate speech.

6. The noise suppression system according to claim 1 , further comprising:
a unit for deriving a noise reducing filter from the provisional estimate speech as corrected and from said noise mean spectrum; and
an estimate speech calculation unit applying filtering by said noise reducing filter to said input signal and obtaining an estimate speech from an output of said noise reducing filter,
wherein said unit for deriving the noise reducing filter includes a unit for transforming said corrected provisional estimate speech derived in a feature vector domain into the spectrum domain.

7. The noise suppression system according to claim 6 , wherein said unit for deriving a noise reducing filter constructs said noise reducing filter, using said input signal in addition to using said provisional estimate speech as corrected and said noise mean spectrum.

8. The noise suppression system according to claim 6 , wherein said unit for deriving a noise reducing filter smoothes the estimate speech as corrected or an a priori SNR, obtained on dividing the corrected estimate speech in at least one of a time direction, a frequency direction, and a direction of a number of dimensions of a feature vector.

9. The noise suppression system according to claim 6 , wherein said unit for deriving a noise reducing filter calculates an a priori SNR η(f, t)
SNR η( f,t )=< S ( f,t )>/ N ( f,t )

where N(f, t) is the noise mean spectrum, <S(f, t)> is the provisional estimate speech, and t is a frame number; and
then constructs a noise reducing filter W(f, t)
W ( f,t )=η( f,t )/(1+η( f,t ))

for the a priori SNR η(f, t); and wherein
said estimate speech calculation unit calculates S(f, t) by a multiplication in a frequency domain:
S ( f,t )= W ( f,t )× X ( f,t )

using said noise reducing filter W(f, t) and the input signal spectrum X(f, t).

10. The noise suppression system according to claim 9 , wherein said unit for deriving a noise reducing filter calculates said a priori SNR η(f, t), t being a frame number, on smoothing, with a use of η(f, t−1) of a directly previous frame, in accordance with
η( f, t )=β×η(f, t−1)+(1−β)×(S(f, t)>/N(f, t), where β is a parameter controlling the smoothing and is such that 0≦β≦1).

11. The noise suppression system according to claim 6 , wherein said unit for deriving a noise reducing filter calculates an a priori SNR η(f, t), on a basis of said noise mean spectrum N(f, t) and on said provisional estimate speech <S(f, t)>, and calculates an a posteriori SNR γ(f, t), on a basis of said noise mean spectrum N(f, t) and said input signal spectrum X(f, t);
said unit for deriving a noise reducing filter uses said noise reducing filter W(f, t) combined with the a priori SNR η(f, t) and the a posteriori SNR γ(f, t); and wherein
said estimate speech calculation unit calculates the estimate speech S(f, t) by a multiplication in a frequency domain of the noise reducing filter W(f, t) and the input signal spectrum X(f, t):
S ( f,t )= W ( f,t )× X ( f,t ),

using said noise reducing filter W(f, t) and the input signal spectrum X(f, t).

12. The noise suppression system according to claim 1 , wherein a control is performed so that a processing of setting an estimate speech obtained by correcting said provisional estimate speech using the reference pattern, as a provisional estimate value, and again correcting the provisional estimate value, using said reference pattern, is carried out a plural number of times.

13. The noise suppression system according to claim 1 , wherein said unit for calculating a noise mean spectrum calculates the spectrum of the noise from at least one of a plurality of input signals, and
wherein said unit for deriving the provisional estimate speech from said input signal and from said noise mean spectrum finds the provisional estimate speech from at least one of said input signals and from said noise spectrum.

14. The noise suppression system according to claim 1 , wherein said unit for correcting said provisional estimate speech calculates an a posteriori probability P(k|S′(f, t)) for the provisional estimate speech S′(f, t), t being a frame number, for the k-th Gaussian distribution, defined by the following equation:
P ( k|S ′( f,t ))= W (k) p (

Assignees

• Arakawa Takayuki
• Tsujikawa Masanori
• Nec Corp

Inventors

• Arakawa Takayuki
• Tsujikawa Masanori

Classifications

• G10L21/0208 Primary

  Noise filtering · CPC title