• The Journal of the Korea Contents Association
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• v.10 no.7
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• pp.470-480
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• 2010

Although e-Learning contents contain audio materials as well as visual materials, research on the design of audio materials has been focused on visual design. If it is considered that non-speech sounds which are a type of audio materials can promptly provide feedbacks of learners' responses and guide learners' learning process, the systemic design of non-speech sounds is needed. Therefore, the purpose of this study is to investigate the learners' perceptions toward non-speech sounds contained the e-Learning contents with multidimensional scaling method. For this purpose, the eleven non-speech sounds were selected among non-speech sounds designed Korea Open Courseware. The 66 juniors in A university responded the degree of similarity among 11 non-speech sounds and the learners' perceptions towards non-speech sounds were represented in the multidimensional space. The result shows that learners perceive separately non-speech sounds by the length of non-speech sounds and the atmosphere which is positive or negative.

• Journal of Broadcast Engineering
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• v.19 no.3
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• pp.296-306
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• 2014

This paper addresses a problem of non-dialog section detection for the DVS authoring, the goal of which is to find meaningful section from the broadcasting audio, where audio description can be inserted. The broadcasting audio involves the presence of various sounds so that it first discriminates between speech and non-speech for each audio frame. Proposed method jointly exploits the inter-channels structure and speech source characteristics of the broadcasting audio whose number of channel is stereo. Also, rule based post-processing is finally applied to detect the non-dialog section whose length is appropriate for audio description. Proposed method provides more accurate detection compared to conventional method. Experimental results on real broadcasting contents show that qualitative superiority of the proposed method.

• Proceedings of the Korea Society of Design Studies Conference
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• 2005.10a
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• pp.36-37
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• 2005

• Journal of Broadcast Engineering
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• v.22 no.5
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• pp.632-642
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• 2017

In this paper, we propose a DNN based speech detection system using acoustic characteristics and context information of media audio. The speech detection for discriminating between speech and non-speech included in the media audio is a necessary preprocessing technique for effective speech processing. However, since the media audio signal includes various types of sound sources, it has been difficult to achieve high performance with the conventional signal processing techniques. The proposed method improves the speech detection performance by separating the harmonic and percussive components of the media audio and constructing the DNN input vector reflecting the acoustic characteristics and context information of the media audio. In order to verify the performance of the proposed system, a data set for speech detection was made using more than 20 hours of drama, and an 8-hour Hollywood movie data set, which was publicly available, was further acquired and used for experiments. In the experiment, it is shown that the proposed system provides better performance than the conventional method through the cross validation for two data sets.

• MALSORI
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• no.61
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• pp.63-74
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• 2007

Discrimination of music and speech from the multimedia signal is an important task in audio coding and broadcast monitoring systems. This paper deals with the problem of feature parameter extraction for discrimination of music and speech. The wavelet transform is a multi-resolution analysis method that is useful for analysis of temporal and spectral properties of non-stationary signals such as speech and audio signals. We propose new feature parameters extracted from the wavelet transformed signal for discrimination of music and speech. First, wavelet coefficients are obtained on the frame-by-frame basis. The analysis frame size is set to 20 ms. A parameter $E_{sum}$ is then defined by adding the difference of magnitude between adjacent wavelet coefficients in each scale. The maximum and minimum values of $E_{sum}$ for period of 2 seconds, which corresponds to the discrimination duration, are used as feature parameters for discrimination of music and speech. To evaluate the performance of the proposed feature parameters for music and speech discrimination, the accuracy of music and speech discrimination is measured for various types of music and speech signals. In the experiment every 2-second data is discriminated as music or speech, and about 93% of music and speech segments have been successfully detected.

• Phonetics and Speech Sciences
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• v.4 no.3
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• pp.25-30
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• 2012

This study investigated whether native Korean listeners could use visual cues for the identification of the English consonants /b/ and /v/. Both auditory and audiovisual tokens of word minimal pairs in which the target phonemes were located in word-initial or word-medial position were used. Participants were instructed to decide which consonant they heard in $2{\times}2$ conditions: cue (audio-only, audiovisual) and location (word-initial, word-medial). Mean identification scores were significantly higher for audiovisual than audio-only condition and for word-initial than word-medial condition. Also, according to signal detection theory, sensitivity, d', and response bias, c were calculated based on both hit rates and false alarm rates. The measures showed that the higher identification rate in the audiovisual condition was related with an increase in sensitivity. There were no significant differences in response bias measures across conditions. This result suggests that native Korean speakers can use visual cues while identifying confusing non-native phonemic contrasts. Visual cues can enhance non-native speech perception.

• Speech Sciences
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• v.8 no.2
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• pp.61-71
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• 2001

Speech material, which is collected from ARS(Automatic Response System), was analyzed and classified into disease and non-disease state. The material include 11 different kinds of diseases. Along with ARS speech, DAT(Digital Audio Tape) speech is collected in parallel to give the bench mark. To analyze speech material, analysis tools, which is developed local laboratory, are used to provide an improved and robust performance to the obtained parameters. To classify speech into disease and non-disease class, multi-layered neural network was used. Three different combinations of 3, 6, 12 parameters are tested to obtain the proper network size and to find the best performance. From the experiment, the classification rate of 92.5% was obtained.

• MALSORI
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• no.60
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• pp.1-11
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• 2006

This paper investigated AV benefit of Korean and Japanese EFL learners' perception of the liquid contrast in English. In a perception experiment, the two English consonants /l/ and /r/ were embedded in initial and medial position in nonsense words in the context of the vowels /i, a, u/. Singletons and clusters were included in the speech material. Audio and video recordings were made using a total of 108 items. The items were presented to Korean and Japanese learners of English in three conditions: audio-alone (A), visual-alone (V) and audio-visual presentation (AV). The results showed that there was no evidence of AV benefit for the perception of the /l/-/r/ contrast for either Korean or Japanese learners of English. The results suggest that increasing auditory proficiency in identifying a non-native contrast is linked with an increasing proficiency in using visual cues to the contrast.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.271-278
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• 2004

In this paper, we propose a content-based audio genre classification algorithm that automatically classifies the query audio into five genres such as Classic, Hiphop, Jazz, Rock, Speech using digital sign processing approach. From the 20 seconds query audio file, the audio signal is segmented into 23ms frame with non-overlapped hamming window and 54 dimensional feature vectors, including Spectral Centroid, Rolloff, Flux, LPC, MFCC, is extracted from each query audio. For the classification algorithm, k-NN, Gaussian, GMM classifier is used. In order to choose optimum features from the 54 dimension feature vectors, SFS(Sequential Forward Selection) method is applied to draw 10 dimension optimum features and these are used for the genre classification algorithm. From the experimental result, we can verify the superior performance of the proposed method that provides near 90% success rate for the genre classification which means 10%∼20% improvements over the previous methods. For the case of actual user system environment, feature vector is extracted from the random interval of the query audio and it shows overall 80% success rate except extreme cases of beginning and ending portion of the query audio file.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.207-210
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• 2007

As the Internet is prevalent in our life, harmful contents have been increasing on the Internet, which has become a very serious problem. Among them, pornographic video is harmful as poison to our children. To prevent such an event, there are many filtering systems which are based on the keyword based methods or image based methods. The main purpose of this paper is to devise a system that classifies the pornographic videos based on the audio information. We use Mel-Cepstrum Modulation Energy (MCME) which is modulation energy calculated on the time trajectory of the Mel-Frequency cepstral coefficients (MFCC) and MFCC as the feature vector and Gaussian Mixture Model (GMM) as the classifier. With the experiments, the proposed system classified the 97.5% of pornographic data and 99.5% of non-pornographic data. We expect the proposed method can be used as a component of the more accurate classification system which uses video information and audio information simultaneously.