• Journal of Broadcast Engineering
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• v.21 no.6
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• pp.977-985
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• 2016

In this paper, we propose a new method for on-line genre classification using spectrogram and deep neural network. For on-line processing, the proposed method inputs an audio signal for a time period of 1sec and classifies its genre among 3 genres of speech, music, and effect. In order to provide the generality of processing, it uses the spectrogram as a feature vector, instead of MFCC which has been widely used for audio analysis. We measure the performance of genre classification using real TV audio signals, and confirm that the proposed method has better performance than the conventional method for all genres. In particular, it decreases the rate of classification error between music and effect, which often occurs in the conventional method.

• The Journal of the Acoustical Society of Korea
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• v.34 no.4
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• pp.257-263
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• 2015

Acoustic identification of inner materials in a single-layer cylindrical shell is investigated with acoustic resonance theory. The theoretical resonance peak frequencies for a cylindrical shell are little affected by the density variation, but remarkably changed by the sound speed variation of inner materials. Such acoustic dependency can be utilized to identify inner materials in a cylindrical shell. Acoustic resonance spectrogram for a single-layer cylindrical shell is theoretically plotted as functions of normalized frequency and sound speed of inner materials. The inner materials can be acoustically identified by overlapping acoustic resonance peaks from measured backscattering sound field on the spectrogram. To experimentally confirm this method, backscattering sound field of cylindrical shell filled with water, oil or ethylene glycol was measured in water tank. The inner materials could be identified by acoustic resonance peaks of the backscattering sound field monostatically measured with a transduce of 1.05 MHz center frequency.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.842-848
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• 2020

Music markets have entered the era of streaming. In order to select and propose music that suits the taste of music consumers, there is an active demand and research on an automatic music genre classification system. We propose a method to improve the accuracy of genre unclassified songs, which was a lack of the previous system, by using a generative adversarial network (GAN) to further develop the automatic voting system for deep learning music genre using Softmax proposed in the previous paper. In the previous study, if the spectrogram of the song was ambiguous to grasp the genre of the song, it was forced to leave it as an unclassified song. In this paper, we proposed a system that increases the accuracy of genre classification of unclassified songs by converting the spectrogram of unclassified songs into an easy-to-read spectrogram using GAN. And the result of the experiment was able to derive an excellent result compared to the existing method.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.845-853
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• 2020

In this paper, we propose a method for classifying environmental sound for selective noise cancellation in industrial sites. Noise in industrial sites causes hearing loss in workers, and researches on noise cancellation have been widely conducted. However, the conventional methods have a problem of blocking all sounds and cannot provide the optimal operation per noise type because of common cancellation method for all types of noise. In order to perform selective noise cancellation, therefore, we propose a method for environmental sound classification based on deep learning. The proposed method uses new sets of acoustic features consisting of temporal and statistical properties of Mel-spectrogram, which can overcome the limitation of Mel-spectrogram features, and uses convolutional neural network as a classifier. We apply the proposed method to five-class sound classification with three noise classes and two non-noise classes. We confirm that the proposed method provides improved classification accuracy by 6.6% point, compared with that using conventional Mel-spectrogram features.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.459-466
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• 2020

The sound collected through the multi-rotor unmanned aerial vehicle (UAV) includes the ego noise generated by the motor or propeller, or the wind noise generated during the flight, and thus the quality is greatly impaired. In a multi-rotor UAV environment, both the magnitude and phase of the target sound are greatly corrupted, so it is necessary to enhance the sound in consideration of both the magnitude and phase. However, it is difficult to improve the phase because it does not show the structural characteristics. in this study, we propose a CNN-based complex spectrogram enhancement method that removes noise based on complex spectrogram that can represent both magnitude and phase. Experimental results reveal that the proposed method improves enhancement performance by considering both the magnitude and phase of the complex spectrogram.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.1
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• pp.162-172
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• 2015

In this paper, we propose an adaptive detection algorithm of footstep and a classification algorithm for activities of the detected footstep. The proposed algorithm can detect and classify whole movement as well as individual and irregular activities, since it does not use continuous footstep signals which are used by most previous research. For classifying movement, we use feature vectors obtained from frequency spectrum from FFT, CWT, AR model and image of AR spectrogram. With SVM classifier, we obtain classification accuracy of single footstep activities over 90% when feature vectors using AR spectrogram image are used.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06c
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• pp.314-317
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• 1994

스펙트럼포락의 변경에 의해 음성의 개인성이 변환될 수 있다는데 착안하여 스펙트럼포락 수정법에 의한 성질변환에 관하여 연구하였다. 실험에서는 남성화자와 여성화자가 각각 발성한 한국어 모음을 대상으로 스펙트럼포락 수정법을 적용하여 스펙트로그램과 청취시험을 비교검토하므로써 성질변환의 성능을 확인하였다.

• Proceedings of the KSLP Conference
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• 2008.03a
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• pp.22-22
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• 2008

• The KIPS Transactions:PartB
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• v.18B no.6
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• pp.397-404
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• 2011

In this paper, we were carried out experiments to apply parameter of voice analysis to measure changing characteristic articulator according to inhale the helium gas. The helium gas was used to overcome air embolism nitrogen gas to deal a fatal blow in body nitrogen gas by diver. However, the helium gas has been much trouble interpretation about abnormal voice of diver to cause squeaky voice of low articulation. Therefor, we was carried out experiments about pitch and spectrogram measurement, analysis based on to influence in acoustic organs before and after of inhaled helium gas.

• Journal of Digital Contents Society
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• v.16 no.2
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• pp.219-225
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• 2015

This experimental study aims at understanding the Korean subjects' phonemic awareness in the English minimal pairs. For the purpose of the experiment, English listening comprehension tests were designed using minimal pairs and conducted among subjects, and the results of the tests were analyzed with the help of spectrogram. From the results of this study, I could find out three important things: First, subjects have difficulty in understanding and distinguishing English vowel minimal pairs. Second, among the English vowel minimal pairs, they had much difficulty in distinguishing between /ə:/ and /ɔ:/. Third, subjects could recognize the semivowel /w/ in words without any difficulty. In addition to this, I tried to analyze the results using the spectrogram, which helps to educate students effectively.