• The Journal of the Acoustical Society of Korea
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• v.28 no.1
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• pp.61-69
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• 2009

This paper presents an improved method measuring inter-utterance similarity in an example-based dialogue system, which searches the most similar utterance in a dialogue database to generate a response to a given user utterance. Unlike general inter-sentence similarity measures, the inter-utterance similarity measure for example-based dialogue system should consider not only word distribution but also various linguistic features, such as affirmation/negation, tense, modality, sentence type, which affects the natural conversation. However, previous approaches do not sufficiently reflect these features. This paper proposes a new utterance similarity measure by analyzing and reflecting various linguistic features to improve performance in accuracy. Also, by considering substitutability of the features, the proposed method can utilize limited number of examples. Experimental results show that the proposed method achieves 10%p improvement in accuracy compared to the previous method.

• The Journal of the Acoustical Society of Korea
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• v.26 no.3E
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• pp.69-73
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• 2007

This paper presents an efficient algorithm to retrieve similar music files from a large archive of digital music database. Users are able to navigate and discover new music files which sound similar to a given query music file by searching for the archive. Since most of the methods for finding similar music files from a large database requires on computing the distance between a given query music file and every music file in the database, they are very time-consuming procedures. By measuring the acoustic distance between the pre-classified music files with the same type of emotion, the proposed method significantly speeds up the search process and increases the precision in comparison with the brute-force method.

• The Journal of the Acoustical Society of Korea
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• v.30 no.8
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• pp.428-435
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• 2011

Many isolated word recognition systems may generate irrelevant words for recognition results because they use only acoustic information or small amount of language information. In this paper, I propose word similarity that is used for selecting (or removing) less common words from candidates by applying Levenshtein distance. Word similarity is obtained by using positional accuracy that reflects the frequency information along to character's alignment information. This paper also discusses various improving techniques of selection of disparate words. The methods include different loss values, phone accuracy based on confusion information, weights of candidates by ranking order and partial comparisons. Through experiments, I found that the proposed methods are effective for removing heterogeneous words without loss of performance.

• The Journal of the Acoustical Society of Korea
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• v.35 no.2
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• pp.83-91
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• 2016

Music-similarity computation is crucial in developing music information retrieval systems for browsing and classification. This paper overviews the recently-proposed centroid-model based music retrieval method and applies the distributional similarity measures to the model for retrieval-performance evaluation. Probabilistic distance measures (also called divergence) compute the distance between two probability distributions in a certain sense. In this paper, we consider the alpha divergence in computing distance between two centroid models for music retrieval. The alpha divergence includes the widely-used Kullback-Leibler divergence and Bhattacharyya distance depending on the values of alpha. Experiments were conducted on both genre and singer datasets. We compare the music-retrieval performance of the distributional similarity with that of the vector distances. The experimental results show that the alpha divergence improves the performance of the centroid-model based music retrieval.

• The Journal of the Acoustical Society of Korea
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• v.12 no.2
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• pp.51-59
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• 1993

본 논문에서는 입력으로 들어 온 레인지데이타에서 특징 추출을 통하여 3차원물체를 식별하는 방법을 제안하고자 한다. Z축 기울기를 이용하여 형상특징을 추출하고, 각 표면조각에서 법선벡터를 구해 기하학적 특징을 추출한다. 그 후 위에서 구한 특징들을 퍼지화데이타로 만들어 일치도 연산에 의해 표준 물체와 입력화상 물체 사이의 정합을 수행한다. 최종적으로 본 논문의 유용성을 실험에 의해 입증하고자 한다.

• The Journal of the Acoustical Society of Korea
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• v.32 no.1
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• pp.79-85
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• 2013

Feature extraction of CASA system uses time continuity and channel similarity and makes correlogram of auditory elements for the use. In case of using feature extraction with cross correlation coefficient for channel similarity, it has much computational complexity in order to display correlation quantitatively. Therefore, this paper suggests feature extraction method using non-parametric correlation coefficient in order to reduce computational complexity when extracting the feature and tests to segregate target speech by CASA system. As a result of measuring SNR (Signal to Noise Ratio) for the performance evaluation of target speech segregation, the proposed method shows a slight improvement of 0.14 dB on average over the conventional method.

• The Journal of the Acoustical Society of Korea
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• v.36 no.6
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• pp.436-441
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• 2017

Computing music similarity is indispensable in constructing music retrieval system. This paper focuses on the cover song search among various music-retrieval tasks. We investigate the cover song search method based on the chroma n-gram to reduce storage for feature DB and enhance search accuracy. Specifically we propose t-tab n-gram, n-gram selection method, and n-gram set comparison method. Experiments on the widely used music dataset confirmed that the proposed method improves cover song search accuracy as well as reduces feature storage.

• The Journal of the Acoustical Society of Korea
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• v.32 no.2
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• pp.174-180
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• 2013

This paper proposes a decision tree based context clustering algorithm for HMM-based speech synthesis systems using the cross likelihood ratio with a hierarchical prior (CLRHP). Conventional algorithms tie the context-dependent HMM states that have similar statistical characteristics, but they do not consider the statistical similarity of split child nodes, which does not guarantee the statistical difference between the final leaf nodes. The proposed CLRHP algorithm improves the reliability of model parameters by taking a criterion of minimizing the statistical similarity of split child nodes. Experimental results verify the superiority of the proposed approach to conventional ones.

• The Journal of the Acoustical Society of Korea
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• v.40 no.2
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• pp.121-129
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• 2021

This paper deals with a super-resolution that improves the resolution of side scan sonar images using learning-based compressive sensing. Learning-based compressive sensing combined with deep learning and compressive sensing takes a structure of a feed-forward network and parameters are set automatically through learning. In particular, we propose a method that can effectively extract additional information required in the super-resolution process through various initialization methods. Representative experimental results show that the proposed method provides improved performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) than conventional methods.

• The Journal of the Acoustical Society of Korea
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• v.37 no.4
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• pp.256-261
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• 2018

There are three types of noise generated inside the vehicle: BSR (Buzz, Squeak, Rattle). In this paper, we propose a classifier that automatically classifies automotive BSR noise by using features extracted from deep convolutional neural networks. In the preprocessing process, the features of above three noises are represented as noise-map using STFT (Short-time Fourier Transform) algorithm. In order to cope with the problem that the position of the actual noise is unknown in the part of the generated noise map, the noise map is divided using the sliding window method. In this paper, internal parameter of the deep convolutional neural networks is visualized using the t-SNE (t-Stochastic Neighbor Embedding) algorithm, and the misclassified data is analyzed in a qualitative way. In order to analyze the classified data, the similarity of the noise type was quantified by SSIM (Structural Similarity Index) value, and it was found that the retractor tremble sound is most similar to the normal travel sound. The classifier of the proposed method compared with other classifiers of machine learning method recorded the highest classification accuracy (99.15 %).