• The Journal of the Acoustical Society of Korea
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• v.26 no.3E
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• pp.104-110
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• 2007

As the exploration of the seabed is extended ever further, automated recognition and classification of sonar images become increasingly important. However, most of the methods ignore the directional information and its effect on the image textures produced. To deal with this problem, we apply 2D Gabor filters to extract the features of sonar images. The filters are designed with constrained parameters to reduce the complexity and to improve the calculation efficiency. Meanwhile, at each orientation, the optimal Gabor filter parameters will be selected with the help of bandwidth parameters based on the Fisher criterion. This method can overcome some disadvantages of the traditional approaches of extracting texture features, and improve the recognition rate effectively.

• Speech Sciences
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• v.14 no.1
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• pp.93-103
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• 2007

The aims of this study were to examine the acoustic features of Korean and Japanese vowels, and to determine whether new phones that do not have counterparts in Japanese or similar phones that have counterparts improve more from learning. This study consisted of three parts. In Experiment I, a speech production test was performed to observe the acoustic features of Korean and Japanese vowels. In Experiment II, the speech production of Korean vowels produced by Koreans, advanced Japanese learners of Korean, and beginning Japanese learners of Korean was investigated. In Experiment III, a speech perception study of Korean vowels produced by the two Japanese learner groups was conducted to observe the effect of learning on acquiring L2 phones. The conclusion drawn from the study was that the similar phones produced by Japanese show more similarity with those of Koreans than new phones in terms of F1 and F2, but Japanese learners of Korean displayed more improvement in new phones from learning.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.2
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• pp.389-396
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• 1999

In this paper, we propose a multiple underwater object classification and tracking algorithm using the narrowband tonal and frequency line features extracted from the frequency spectrum of the acoustic signal. The general algorithm using the wideband and narrowband energy has a high tracking error when objects are close and cross each other. But the proposed algorithm shows a good tracking performance for the simulation scenarios generated by the real acoustic data.

• Speech Sciences
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• v.13 no.2
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• pp.19-33
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• 2006

The purpose of this paper was to examine acoustical characteristics of phonatory offset-onset mechanism in the connected speech of female adults with stuttering and normal nonfluency. The phonatory offset-onset mechanism refers to the laryngeal articulatory gestures. Those gestures are required to mark word boundaries in phonetic contexts of the connected speech. This mechanism included 7 patterns based on the speech spectrogram. This study showed the acoustic features in the connected speech in the production of female adults with stuttering (n=1) and normal nonfluency (n=3). Speech tokens in V_V, V_H, and V_S contexts were selected for the analysis. Speech samples were recorded by Sound Forge, and the spectrographic analysis was conducted using Praat. Results revealed a stuttering (with a type of block) female exhibited more laryngealization gestures in the V_V context. Laryngealization gesture was more characterized by a complete glottal stop or glottal fry both in V_H and in V_S contexts. The results were discussed from theoretical and clinical perspectives.

• The Journal of the Acoustical Society of Korea
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• v.18 no.2E
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• pp.39-44
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• 1999

In passive sonar, underwater moving objects are identified by the acoustic sounds they transmit. The spectrum of these sounds show features about the mechanism of the sound source, these features are discrete frequencies on the spectrum and frequency lines on the spectrogram. Variability in the underwater environment produce discontinuous broken or unstable fluctuating frequency lines. In this paper, we propose an efficient algorithm that estimate continuities of the discontinuous frequency lines and extract presence of the unstable frequency lines using neural networks and represent the proposed algorithm shows good performance in estimation and extraction the unstable frequency lines through the experiments.

• ETRI Journal
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• v.30 no.5
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• pp.753-755
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• 2008

In this letter, we propose a new histogram equalization technique for feature compensation in speech recognition under noisy environments. The proposed approach combines a signal-to-noise-ratio-dependent feature reconstruction method and the class histogram equalization technique to effectively reduce the acoustic mismatch present in noisy speech features. Experimental results from the Aurora 2 task confirm the superiority of the proposed approach for acoustic feature compensation.

• Proceeding of Spring/Autumn Annual Conference of KHA
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• 2004.11a
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• pp.335-341
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• 2004

According to the improvements of the education and the cultural level, the noise pollutions which have been occupying a major portion of civil petitions about environment is gradually aggravating. Especially, the plumbing noises which took place at machine room of dormitory are the compositive shapes of an air-borne sounds and a solid-borne sounds. So it has been causing to injure the comfortable residential environment of residents that it is propagated in a residential space. Judging from this point of view, this study grasped the propagation and the properties of attenuation about four varieties's plumbing noise which took place at machine room to understand that it cause influences to a residential space. In this point, we understand the peculiar features by measuring noise, which was generated from equipment in machine rooms of three dormitories having different features. On the basis of these features, we examine all predictability and reliability in comparing the predictive value with the measurable one, using architectural acoustic simulation.

• The Journal of the Acoustical Society of Korea
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• v.40 no.4
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• pp.337-346
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• 2021

Detection of abnormal signal generally can be done by using features of normal signals as main information because of data imbalance. This paper propose an efficient method for abnormal signal detection using parallel AutoEncoder (AE) which can use features of abnormal signals as well. The proposed Parallel AE (PAE) is composed of a normal and an abnormal reconstructors having identical AE structure and train features of normal and abnormal signals, respectively. The PAE can effectively solve the imbalanced data problem by sequentially training normal and abnormal data. For further detection performance improvement, additional binary classifier can be added to the PAE. Through experiments using public acoustic data, we obtain that the proposed PAE shows Area Under Curve (AUC) improvement of minimum 22 % at the expenses of training time increased by 1.31 ~ 1.61 times to the single AE. Furthermore, the PAE shows 93 % AUC improvement in detecting abnormal underwater acoustic signal when pre-trained PAE is transferred to train open underwater acoustic data.

• The Journal of the Acoustical Society of Korea
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• v.33 no.4
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• pp.248-254
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• 2014

Among acoustic signal analysis tasks, acoustic context awareness is one of the most formidable tasks in terms of complexity since it requires sophisticated understanding of individual acoustic events. In conventional context awareness methods, individual acoustic event detection or recognition is employed to generate a relevant decision on the impending context. However this approach may produce poorly performing decision results in practical situations due to the possibility of events occurring simultaneously or the acoustically similar events that are difficult to distinguish with each other. Particularly, the babble noise acoustic event occurring at a bus or subway environment may create confusion to context awareness task since babbling is similar in any environment. Therefore in this paper, a frequency-cepstral feature vector is proposed to mitigate the confusion problem during the situation awareness task of binary decisions: bus or metro. By employing the Support Vector Machine (SVM) as the classifier, the proposed feature vector scheme is shown to produce better performance than the conventional scheme.

• Phonetics and Speech Sciences
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• v.8 no.4
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• pp.103-113
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• 2016

This paper aims to propose articulatory features as novel predictors for automatic pronunciation assessment of English produced by Korean learners. Based on the distinctive feature theory, where phonemes are represented as a set of articulatory/phonetic properties, we propose articulatory Goodness-Of-Pronunciation(aGOP) features in terms of the corresponding articulatory attributes, such as nasal, sonorant, anterior, etc. An English speech corpus spoken by Korean learners is used in the assessment modeling. In our system, learners' speech is forced aligned and recognized by using the acoustic and pronunciation models derived from the WSJ corpus (native North American speech) and the CMU pronouncing dictionary, respectively. In order to compute aGOP features, articulatory models are trained for the corresponding articulatory attributes. In addition to the proposed features, various features which are divided into four categories such as RATE, SEGMENT, SILENCE, and GOP are applied as a baseline. In order to enhance the assessment modeling performance and investigate the weights of the salient features, relevant features are extracted by using Best Subset Selection(BSS). The results show that the proposed model using aGOP features outperform the baseline. In addition, analysis of relevant features extracted by BSS reveals that the selected aGOP features represent the salient variations of Korean learners of English. The results are expected to be effective for automatic pronunciation error detection, as well.