• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.199-206
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• 2017

Environmental noise can degrade the performance of speech recognition system. This paper presents a procedure for performing cepstrum based feature compensation to make recognition system robust to noise. The approach is based on direct compensation of spectral tilt to remove effects of additive noise. The noise compensation scheme operates in the cepstral domain by means of calculating spectral tilt of the log power spectrum. Spectral compensation is applied in combination with SNR-dependent cepstral mean compensation. Experimental results, in the presence of white Gaussian noise, subway noise and car noise, show that the proposed compensation method achieves substantial improvements in recognition accuracy at various SNR's.

• The Journal of the Acoustical Society of Korea
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• v.20 no.6
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• pp.26-33
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• 2001

Though speaker recognition based on conventional AR HMM shows good performance, its lack of modeling the environmental noise makes its performance degraded in case of practical noisy environment. In this paper, a robust speaker recognition system based on AR HMM is proposed, where noise is considered in the observation signal model for practical noisy environment and duration-term is considered to increase performance. Experimental results, using the digits database from 100 speakers (77 males and 23 females) under white noise and car noise, show improved performance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.165-172
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• 2005

In speech recognition under a noisy environment, it is necessary to construct a system which reduces the noise and enhances the speech. Then it is effective to imitate the human auditory system which has an excellent analytical spectrum mechanism for speech enhancement. Accordingly, this paper proposes an adaptive method using the auditory mechanism which is called lateral inhibition. This method first estimates the noise intensity by neural network, then adaptively adjusts both the coefficients of the lateral inhibition and the adjusting coefficient of amplitude component according to the noise intensity for each input frame. It is confirmed that the proposed method is effective for speech degraded by white noise, colored noise, and road noise based on the spectral distortion measurement.

• The Journal of the Acoustical Society of Korea
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• v.6 no.4
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• pp.12-20
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• 1987

Since People have serveral feelings for the sound according to its frequency the responses of sound frequencies for the people have been studies in this paper. The feelings of sound are investigated by questionaire thar are pure tones, waring sounds of automobile and sounds that white noise is passed by one and one-third octave bandpass filter. Experimental results have been shown that people have good response for the pure tone between 160(Hz) and 500(Hz), and have unpleasant response for the pure tone above 1000 (Hz), warning sounds of automobile, and for white noise. Warning sound of automobile horn has been mainly distributed between 1000(Hz) and 2600(Hz). Hence the resells are shown that the responses for warning sound of autombile horn are similar to that for pure tone between 100(Hz) and 2600(Hz). As a results, it is necessary to make warning sound of automobile horn have frequencies between 200(Hz) and 500(Hz) with low level in the residental districts and crowded streets.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.2 no.1
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• pp.35-43
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• 2009

In this paper, we introduce a reading training program offering visual-auditory cue with noise cancellation function (RT program) developed by us. The RT program provides some training sentences with visual-auditory cues. Motor speech disorder patients can use the visual and/or auditory cues for reading training. To provide convenient estimation of training result, we developed a noise cancellation algorithm. The function of the algorithm is to remove noise and auditory-cues which are recorded with reading speech at the same time while patient read the sentences in PC monitor. In addition, we developed a function for finding out the first starting time of reading sound after a patient sees a sentence and begins to read the sentence. The recorded speeches are acquired from six people(three male, three female) in four noisy environments (interior noise, white noise, car interior noise, babble noise). We evaluated the timing error for starting time between original recorded speech and processed speech in condition of executing noise cancellation function and not executing. The timing error was improved as much as $4.847{\pm}2.4235[ms]$ as the effect of noise cancellation. It is expected that the developed RT program helps motor speech disorder patient in reading training and symptom evaluation.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.9
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• pp.65-71
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• 2004

This paper describes a new adaptive algorithm which can reduce the required computation quantities in the adaptive filter. The proposed S-LMS algorithm uses only the signs of the normalized input signal rather than the input signals when coefficients of the filter are adapted. By doing so, there is no need for the multiplications and divisions which are mostly responsible for the computation quantities. To analyze the convergence characteristics of the proposed algorithm, the condition and speed of the convergence are derived mathematically. Also, we simulate an echo canceller adopting this algorithm and compare the performances of convergence for this algorithm with the ones for the other algorithm. As the results of simulations, it is proved that the echo canceller adopting this algorithm shows almost the same performances of convergence as the echo canceller adopting the SIA algorithm.

• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.187-196
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• 2011

Induction motors play a vital role in aeronautical and automotive industries so that many researchers have studied on developing a fault detection and classification system of an induction motor to minimize economical damage caused by its fault. With this reason, this paper extracts robust feature vectors from the normal/abnormal vibration signals of the induction motor in noise circumstance: partial autocorrelation (PARCOR) coefficient, log spectrum powers (LSP), cepstrum coefficients mean (CCM), and mel-frequency cepstrum coefficient (MFCC). Then, we classified different types of faults of the induction motor by using the extracted feature vectors as inputs of a neural network. To find optimal feature vectors, this paper evaluated classification performance with 2 to 20 different feature vectors. Experimental results showed that five to six features were good enough to give almost 100% classification accuracy except features by CCM. Furthermore, we considered that vibration signals could include noise components caused by surroundings. Thus, we added white Gaussian noise to original vibration signals, and then evaluated classification performance. The evaluation results yielded that LSP was the most robust in noise circumstance, then PARCOR and MFCC followed by LSP, respectively.