• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• 제21권10호
• /
• pp.960-966
• /
• 2011

An improved NDIF method is introduced to efficiently extract eigenvalues and eigenmodes of two-dimensional, arbitrarily shaped acoustic cavities. The NDIF method, which was developed by the authors for the eigen-mode analysis of arbitrarily shaped acoustic cavities, membranes, and plates, has the feature that it yields highly accurate eigenvalues compared with other analytical methods or numerical methods(FEM and BEM). However, the NDIF method has the weak point that the system matrix of the NDIF method depends on the frequency parameter and, as a result, a final system equation doesn's take the form of an algebra eigenvalue problem. The system matrix of the improved NDIF method developed in the paper is independent of the frequency parameter and eigenvalues and mode shapes can be efficiently obtained by solving a typical algebraic eigenvalue problem. Finally, the validity and accuracy of the proposed method is verified in two case studies, which indicate that eigenvalues and mode shapes obtained by the proposed method are very accurate compared to the exact method, the NDIF method or FEM(ANSYS).

• Phonetics and Speech Sciences
• /
• 제3권3호
• /
• pp.57-62
• /
• 2011

Focused items in English sentences are usually accompanied by changes in acoustic manifestation. This paper investigates the acoustic characteristics of $H^*$ in English utterances produced by natives speakers of English and Korean learners of English. To obtain more reliable results, the changes of the acoustic feature values (F0, intensity, syllable duration) were normalized by a median value and a whole duration of each utterance. Acoustic values of sentences with no focused words were compared with those of sentences with focused words within each group (Americans vs. Koreans). Sentences with focused words were compared between the two groups, too. In the instances in which a significant Group x Focus Location (initial, middle and final of a sentence) interaction was obtained, further analysis testing the effect of Group on each Focus Location was conducted. The analysis revealed that Korean learners of English produced focused words with lower F0, lower intensity and shorter syllable duration than native speakers of English. However, the effect of intensity change caused by focus was not significant within each group. Further analysis examining the interaction of Group and Focus Location showed that the change in F0 produced by Korean group was significantly lower in the middle and the final positions of sentences than by American group. Implications for the intonation training were also discussed.

• The Journal of the Acoustical Society of Korea
• /
• 제32권2호
• /
• pp.167-173
• /
• 2013

This work examines classification of diphthongs, as part of a distinctive feature-based speech recognition system. Acoustic measurements related to the vocal tract and the voice source are examined, and analysis of variance (ANOVA) results show that vowel duration, energy trajectory, and formant variation are significant. A balanced error rate of 17.8% is obtained for 2-way diphthong classification on the TIMIT database, and error rates of 32.9%, 29.9%, and 20.2% are obtained for /aw/, /ay/, and /oy/, for 4-way classification, respectively. Adding the acoustic features to widely used Mel-frequency cepstral coefficients also improves classification.

• Journal of Broadcast Engineering
• /
• 제17권3호
• /
• pp.519-528
• /
• 2012

Voice activity detection (VAD) is generally conducted by extracting features from the acoustic signal and a decision rule. The performance of such VAD algorithms driven by the input acoustic signal highly depends on the acoustic noise. When video signals are available as well, the performance of VAD can be enhanced by using the visual information which is not affected by the acoustic noise. Previous visual VAD algorithms usually use single visual feature to detect the lip activity, such as active appearance models, optical flow or intensity variation. Based on the analysis of the weakness of each feature, we propose to combine intensity change measure and the optical flow in the mouth region, which can compensate for each other's weakness. In order to minimize the computational complexity, we develop simple measures that avoid statistical estimation or modeling. Specifically, the optical flow is the averaged motion vector of some grid regions and the intensity variation is detected by simple thresholding. To extract the mouth region, we propose a simple algorithm which first detects two eyes and uses the profile of intensity to detect the center of mouth. Experiments show that the proposed combination of two simple measures show higher detection rates for the given false positive rate than the methods that use a single feature.

• Journal of Biomedical Engineering Research
• /
• 제16권3호
• /
• pp.345-352
• /
• 1995

The LPC cepstrum coefficients, which are an acoustic features of speech signal, have been widely used as the feature parameter for various speech recognition systems and showed good performance. The vocal tract area function is a kind of articulatory feature, which is related with the physiological mechanism of speech production. This paper proposes the vocal tract area function as an alternative feature parameter for speech recognition. The linear predictive analysis using Burg algorithm and the vector quantization are performed. Then, recognition experiments for 5 Korean vowels and 10 digits are executed using the conventional LPC cepstrum coefficients and the vocal tract area function. The recognitions using the area function showed the slightly better results than those using the conventional LPC cepstrum coefficients.

• Journal of the Korean Society of Safety
• /
• 제27권3호
• /
• pp.20-27
• /
• 2012

Mechanical seals are one of main components of high speed centrifugal pumps. So, it is very important to detect the faults (scratch, notch, indentation, wear) of mechanical seals since the damage of seal can cause a critical failures or accidents of machinery system. In the past, many researchers mainly performed to detect the seal fault using the time signals measured from sensors. Recently, studies are focused on the development of on-line real time monitoring system. But study on the feature parameters used for fault detection of mechanical seals has a little been performed. In this paper, we showed feature parameters extracted from accelerated and acoustic signals by using the discrete wavelet transform (DWT), alpha coefficient, statistical parameters. And also verified the possibility for fault detection of mechanical seal.

• MALSORI
• /
• 제55권
• /
• pp.131-142
• /
• 2005

A mismatch between the training and the test conditions often causes a drastic decrease in the performance of the speech recognition systems. In this paper, non-linear transformation techniques based on histogram equalization in the acoustic feature space are studied for reducing the mismatched condition. The purpose of histogram equalization(HEQ) is to convert the probability distribution of test speech into the probability distribution of training speech. While conventional histogram equalization methods consider only the probability distribution of a test speech, for noise-corrupted test speech, its probability distribution is also distorted. The transformation function obtained by this distorted probability distribution maybe bring about miss-transformation of feature vectors, and this causes the performance of histogram equalization to decrease. Therefore, this paper proposes a new method of calculating noise-removed probability distribution by using assumption that the CDF of noisy speech feature vectors consists of component of speech feature vectors and component of noise feature vectors, and this compensated probability distribution is used in HEQ process. In the AURORA-2 framework, the proposed method reduced the error rate by over $44\%$ in clean training condition compared to the baseline system. For multi training condition, the proposed methods are also better than the baseline system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 한국소음진동공학회 2007년도 추계학술대회논문집
• /
• pp.891-894
• /
• 2007

This study presents fault diagnosis of low speed bearing using support vector machine (SVM). The data used in the experiment was acquired using acoustic emission (AE) sensor and accelerometer. The aim of this study is to compare the performance of fault diagnosis based on AE signal and vibration signal with same load and speed. A low speed test rig was developed to simulate various defects with shaft speeds as low as 10 rpm under several loading conditions. In this study, component analysis was also performed to extract the feature and reduce the dimensionality of original data feature. Moreover, the classification for fault diagnosis was also conducted using original data feature without feature extraction. The result shows that extracted feature from AE sensor gave better performance in faults classification.

• Journal of Information Processing Systems
• /
• 제13권5호
• /
• pp.1135-1148
• /
• 2017

Speech recognition is one of the fascinating fields in the area of Computer science. Accuracy of speech recognition system may reduce due to the presence of noise present in speech signal. Therefore noise removal is an essential step in Automatic Speech Recognition (ASR) system and this paper proposes a new technique called combined thresholding for noise removal. Feature extraction is process of converting acoustic signal into most valuable set of parameters. This paper also concentrates on improving Mel Frequency Cepstral Coefficients (MFCC) features by introducing Discrete Wavelet Packet Transform (DWPT) in the place of Discrete Fourier Transformation (DFT) block to provide an efficient signal analysis. The feature vector is varied in size, for choosing the correct length of feature vector Self Organizing Map (SOM) is used. As a single classifier does not provide enough accuracy, so this research proposes an Ensemble Support Vector Machine (ESVM) classifier where the fixed length feature vector from SOM is given as input, termed as ESVM_SOM. The experimental results showed that the proposed methods provide better results than the existing methods.

• Journal of IKEEE
• /
• 제13권2호
• /
• pp.132-139
• /
• 2009

In this paper, a speech feature based double-talk detector method is proposed for an acoustic echo cancellation in hands-free communication system. The double-talk detector is an important element, since it controls the update of the adaptive filter for an acoustic echo cancellation. In previous research, the double talk detector is considered in the signal processing stage without taking the speech characteristics into account. However, in the proposed method, speech features which are used for the speech recognition is used for the discriminative features between the far-end and near-end speech. We obtained a substantial improvement over the previous double-talk detector methods using the only signal in time domain.