• Journal of information and communication convergence engineering
• /
• 제10권2호
• /
• pp.162-167
• /
• 2012

This paper proposes a speech processing system based on a model of the human auditory system and a noise reduction neural network with fast Fourier transform (FFT) amplitude and phase spectrums for noise reduction under background noise environments. The proposed system reduces noise signals by using the proposed neural network based on FFT amplitude spectrums and phase spectrums, then implements auditory processing frame by frame after detecting voiced and transitional sections for each frame. The results of the proposed system are compared with the results of a conventional spectral subtraction method and minimum mean-square error log-spectral amplitude estimator at different noise levels. The effectiveness of the proposed system is experimentally confirmed based on measuring the signal-to-noise ratio (SNR). In this experiment, the maximal improvement in the output SNR values with the proposed method is approximately 11.5 dB better for car noise, and 11.0 dB better for street noise, when compared with a conventional spectral subtraction method.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 1998년도 하계종합학술대회논문집
• /
• pp.599-602
• /
• 1998

In this paper, a speech enhancement method based on phonemic properties and masking effect is propsoed. It is a modified type of spectral subtraction wherein the spectral sharpening process is exploited in unvoiced state considering the phonemic properties. The masking threshold is used to remove the residual noise. The proposed spectral subtraction shows similar performance as that of the classical spectral subtraction method in view of the SNR. But by the prposed scheme, the unvoiced sound region is shown to exhibit relatively less signal distortion in the enhanced speech.

• 음성과학
• /
• 제15권2호
• /
• pp.67-77
• /
• 2008

In this paper, we implemented a robust speech recognizer using the TMS320VC33 DSP. For this implementation, we had built speech and noise database suitable for the recognizer using spectral subtraction method for noise removal. The recognizer has an explicit structure in aspect that a speech signal is enhanced through spectral subtraction before endpoints detection and feature extraction. This helps make the operation of the recognizer clear and build HMM models which give minimum model-mismatch. Since the recognizer was developed for the purpose of controlling car facilities and voice dialing, it has two recognition engines, speaker independent one for controlling car facilities and speaker dependent one for voice dialing. We adopted a conventional DTW algorithm for the latter and a continuous HMM for the former. Though various off-line recognition test, we made a selection of optimal conditions of several recognition parameters for a resource-limited embedded recognizer, which led to HMM models of the three mixtures per state. The car noise added speech database is enhanced using spectral subtraction before HMM parameter estimation for reducing model-mismatch caused by nonlinear distortion from spectral subtraction. The hardware module developed includes a microcontroller for host interface which processes the protocol between the DSP and a host.

• 대한임베디드공학회논문지
• /
• 제3권1호
• /
• pp.1-7
• /
• 2008

In this paper, a MSS-NOVO method that combines the HMM composition method with a noise reduction method is proposed for speech recognition in noisy environments. This combined method starts with noise reduction with modified spectral subtraction (MSS) to enhance the input noisy speech, then the noise and voice composition (NOVO) method is applied for making noise adapted models by using the noise in the non-utterance regions of the enhanced noisy speech. In order to evaluate the effectiveness of our proposed method, we compare MSS-NOVO method with other methods, i.e., SS-NOVO, MWF-NOVO. To set up the noisy speech for test, we add White noise to KLE 452 database with different SNRs range from 0dB to 15dB, at 5dB intervals. From the tests, MSS-NOVO method shows average improvement of 66.5% and 13.6% compared with the existing SS-NOVO method and MWF-NOVO method, respectively. Especially our proposed MSS-NOVO method shows a big improvement at low SNRs.

• Imaging Science in Dentistry
• /
• 제31권1호
• /
• pp.23-34
• /
• 2001

Purpose : To develop a new subtraction program for registering digital periapical images based on the correspondence of anatomic structures. Materials and Methods: The digital periapical images were obtained by Digora system with Rinn XCP equipment after translation of 1-16 mm, and rotation of 2-20° at the premolar and molar areas of the human dried mandible. The new subtraction program, NIH Image program and Emago/Advanced program were compared by the peak-signal-to noise ratio (PSNR). Results : The new subtraction program was superior to NIH Images program and Emagol Advanced program up to 16 mm translation and horizontal angulation up to 4°. Conclusion: The new subtraction program can be used for subtracting digital periapical images.

• JSTS:Journal of Semiconductor Technology and Science
• /
• 제16권3호
• /
• pp.375-379
• /
• 2016

A current subtraction technique with parallel operation system is proposed to remove excessive current in touch screen application. The proposed current subtraction remove the current which go into the input node of charge amplifier. The value of subtraction current is same with current when touch screen is not touched. As a result, charge amplifier output is only proportional to variation of mutual capacitor, which make dynamic rage is increased. Also, Transmitter (Tx) driving signal and subtraction driving signal are out of phase each other. Thus, noise generated in Tx is cancelled. The proposed IC is implemented in a mixed-mode 0.18-um CMOS process. Overall system is designed for touch screen panel (TSP) with 16 driving lines and 8 sensing lines. 5-V supply voltages are used in the proposed circuits. For multiple Tx driving signal, Walsh codes are used and signal frequency is 300 khz. By using proposed technique, dynamic rage is improved 36 dB.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
• /
• pp.511-514
• /
• 2003

The human being uses speech signals to exchange information. When background noise is present, speech recognizers experience performance degradations. Speech recognition through speech enhancement in the noisy environment was studied. Histogram method as a reliable noise estimation approach for spectral subtraction was introduced using MFCC method. The experiment results show the effectiveness of the proposed algorithm.

• 한국음향학회:학술대회논문집
• /
• 한국음향학회 1985년도 학술발표회 논문집
• /
• pp.87-94
• /
• 1985

In this paper, enhancement of speech corrupted by additive white or colored noise is stuided. The nuconstrained frequency-domain block least-mean-square (UFBLMS) adaptation algorithm and its frequency-weighted version are newly applied to speech enhancement. For enhancement of speech degraded by white noise, the performance of the UFBLMS algorithm is superior to the spectral subtraction method or Wiener filtering technique by more than 3 dB in segmented frequency-weighted signal-to-noise ratio(FWSNERSEG) when SNR of speech is in the range of 0 to 10 dB. As for enhancement of noisy speech corrupted by colored noise, the UFBLMS algorithm is superior to that of the spectral subtraction method by about 3 to 5 dB in FWSNRSEG. Also, it yields better performance by about 2 dB in FWSNR and FWSNRSEG than that of time-domain least-mean-square (TLMS) adaptive prediction filter(APF). In view of the computational complexity and performance improvement in speech quality and intelligibility, the frequency-weighted UFBLMS algorithm appears to yield the best performance among various algorithms in enhancing noisy speech corrupted by white or colored noise.

• 한국음향학회지
• /
• 제40권5호
• /
• pp.439-451
• /
• 2021

본 논문에서는 실제 환경에서의 연속 음성 강화를 위한 딥러닝 기반 온라인 빔포밍 알고리듬과 스펙트럼 감산을 결합한 빔포머를 제안한다. 기존 빔포밍 시스템은 컴퓨터에서 음성과 잡음을 완전히 겹친 방식으로 혼합하여 생성된 사전 분할 오디오 신호를 사용하여 대부분 평가되었다. 하지만 실제 환경에서는 시간 축으로 음성 발화가 띄엄띄엄 발성되기 때문에, 음성이 없는 잡음 신호가 시스템에 입력되면 기존 빔포밍 알고리듬의 성능이 저하된다. 이러한 효과를 경감하기 위하여, 심층 학습 기반 온라인 빔포밍 알고리듬과 스펙트럼 감산을 결합하였다. 잡음 환경에서 온라인 빔포밍 알고리듬을 평가하기 위해 연속 음성 강화 세트를 구성하였다. 평가 세트는 CHiME3 평가 세트에서 추출한 음성 발화와 CHiME3 배경 잡음 및 MUSDB에서 추출한 연속 재생되는 배경음악을 혼합하여 구성되었다. 음성인식기로는 Kaldi 기반 툴킷 및 구글 웹 음성인식기를 사용하였다. 제안한 온라인 빔포밍 알고리듬 과 스펙트럼 감산이 베이스라인 빔포밍 알고리듬에 비해 성능 향상을 보임을 확인하였다.

• ETRI Journal
• /
• 제37권3호
• /
• pp.637-645
• /
• 2015

Common-mode noise (CMN) is an unresolved problem in wireless capsule endoscopy (WCE) systems. In a WCE system, CMN originates from various electric currents found within the human body or external interference sources and causes critical demodulation performance degradation. The differential operation, a typical method for the removal of CMN rejection, can remove CMN by subtracting two signals simultaneously received by two reception sensors attached to a human body. However, when there is impedance mismatching between the two reception sensors, the differential operation method cannot completely remove CMN. Therefore, to overcome this problem, we propose an enhanced CMN rejection method. The proposed method performs not only subtraction but also addition between two received signals. Then a CMN ratio can be estimated by sufficient accumulation of division operation outcomes between the subtraction and addition outputs during the guard period. Finally, we can reject the residual CMN by combining the subtraction and addition outputs.