• Journal of Ocean Engineering and Technology
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• v.33 no.5
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• pp.421-426
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• 2019

The directivity of an underwater sound source should be measured in an acoustically open field such as a calm sea or lake, or an anechoic water tank facility. However, technical difficulties arise when practically implementing this in open fields. Signal processing-based techniques such as a sound intensity method and near-field acoustic holography have been adopted to overcome the problem, but these are inefficient in terms of acquisition and maintenance costs. This study established a simple directivity estimation technique with data acquisition, filtering, and analysis tools. A numerical simulation based on an acoustic radiosity method showed that the technique is practicable for sound source directivity estimation in a diffused reverberant acoustic field like a reverberant water tank.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1606-1613
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• 2021

Recently various attempts to control hardware through integration of sensors and artificial intelligence have been made. This paper proposes a smart hazardous sound detection at home. Previous sound recognition methods have problems due to the processing of background sounds and the low recognition accuracy of high-frequency sounds. To get around these problems, a new MFCC(Mel-Frequency Cepstral Coefficient) algorithm using Wiener filter, modified filterbank is proposed. Experiments for comparing the performance of the proposed method and the original MFCC were conducted. For the classification of feature vectors extracted using the proposed MFCC, DNN(Deep Neural Network) was used. Experimental results showed the superiority of the modified MFCC in comparison to the conventional MFCC in terms of 1% higher training accuracy and 6.6% higher recognition rate.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.273-281
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• 2022

In this paper, we propose a robot sound recognition system that detects various sound events. The proposed system is designed to detect various sound events in real-time by using a microphone on a robot. To get real-time performance, we use a VGG11 model which includes several convolutional neural networks with real-time normalization scheme. The VGG11 model is trained on augmented DB through 24 kinds of various environments (12 reverberation times and 2 signal to noise ratios). Additionally, based on random forest algorithm, a decision logic is also designed to generate event signals for robot applications. This logic can be used for specific classes of acoustic events with better performance than just using outputs of network model. With some experimental results, the performance of proposed sound recognition system is shown on real-time device for robots.

• The Journal of the Acoustical Society of Korea
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• v.29 no.3
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• pp.191-199
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• 2010

Physical modeling is a synthesis method of high quality sound which is similar to real sound for musical instruments. However, since physical modeling requires a lot of parameters to synthesize sound of a musical instrument, it prevents real-time processing for the musical instrument which supports a large number of sounds simultaneously. To solve this problem, this paper proposes a single instruction multiple data (SIMD) parallel processor that supports real-time processing of sound synthesis of guitar, a representative plucked string musical instrument. To control six strings of guitar, we used a SIMD parallel processor which consists of six processing elements (PEs). Each PE supports modeling of the corresponding string. The proposed SIMD processor can generate synthesized sounds of six strings simultaneously when a parallel synthesis algorithm receives excitation signals and parameters of each string as an input. Experimental results using a sampling rate 44.1 kHz and 16 bits quantization indicate that synthesis sounds using the proposed parallel processor were very similar to original sound. In addition, the proposed parallel processor outperforms commercial TI's TMS320C6416 in terms of execution time (8.9x better) and energy efficiency (39.8x better).

• KIPS Transactions on Software and Data Engineering
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• v.3 no.2
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• pp.81-86
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• 2014

In this paper, we present an effective sound classification system for recognizing the real-time context of a smartphone user. Our system avoids unnecessary consumption of limited computational resource by filtering both silence and white noise out of input sound data in the pre-processing step. It also improves the classification performance on low energy-level sounds by amplifying them as pre-processing. Moreover, for efficient learning and application of HMM classification models, our system executes the dimension reduction and discretization on the feature vectors through k-means clustering. We collected a large amount of 8 different type sound data from daily life in a university research building and then conducted experiments using them. Through these experiments, our system showed high classification performance.

• Journal of Ship and Ocean Technology
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• v.8 no.3
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• pp.18-25
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• 2004

Sound reception system is required to detect the sound and the quadrantal direction of the other ship's horn sound, to overcome the effects of enclosed wall for navigation space, functioning as a sound barrier. However, the realized systems can only provide quadrantal information of the other ship. This paper presents a new arrangement of microphones, having geometrically symmetric deployment with the same distances between sensors and the same angles between adjacent sensors with respect to the geometrical center. The sound pressures received at microphones are transformed into the related envelope signals by applying Hilbert transform. The time delays between microphones are estimated by the correlation functions between the derived envelope signals. This envelope base processing mitigates the noises related to the reflection by ship and sea surface. Then, the directional information is easily defined by using the estimated time delays. The suggested method is verified by the generated signals using boundary element method for a small ship model with sea surface wave. The estimated direction is quite similar to the true one and therefore the proposed approach can be used as an efficient sound reception system.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.796-801
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• 1994

Measuring noise, sound quality or acoustical comfort presents a difficult task for the acoustic engineer. Sound and noise are ultimately jugded by human beings acting as analysers. Regulations for determining noise levels are based on A-weighted SPL measurement performed with only one microphone. This method of measurement is usually specified when determining whether the ear can be physically damaged. Such a simple measurement procedure is not able to determine annoyance of sound events or sound quality in general. For some years investigations with binaural measurement analysis technique have shown new possibilities for the objective determination of sound quality. By using Artificial Head technology /1/, /2/ in conjunction with psychoacoustic evaluation algorithms - and taking into account binaural signal processing of human hearing, considerable progress regarding the analysis of sounds has been made. Because sound events often arise in a complex way, direct conclusions about components subjectively judged to be annoying with regard to their causes and transmission paths, can be drawn in a limited way only. A new procedure, complementing binaural measurement technology combined with mulit-channel measuements of acceleration sensor signals has been developed. This involves correlating signals influencing sound quality, analyzed by means of human hearing, with signals form different acceleration sensors fixed at different positions of the sound source. Now it is possible to recognize the source and the transmission way of those signals which have an influence on the annoyance of sound.

• Journal of Korea Game Society
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• v.16 no.2
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• pp.119-128
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• 2016

In computer games, most of GUI methods are keyboards, mouses and touch screens. The total time of processing the sound commands for games is the sum of input time and recognition time. In this paper, we propose a method for taking only the prefixes of the input signals for sound commands, resulting in the reduced the total processing time, instead of taking the whole input signals. In our method, command sounds are recognized using HMM(Hidden Markov Model), where separate HMM's are built for the whole input signals and their prefix signals. We experiment our proposed method with representative commands of platform games. The experiment shows that the total processing time of input command signals reduces without decreasing recognition rate significantly. The study will contribute to enhance the versatility of GUI for computer games.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.4
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• pp.101-107
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• 1997

In order to detect and suppress chatter in turning process, a stability control methodology was studied through manipulation of spindle speeds regarding to chatter frequencies, The chatter frequency was identified by monitoring and signal processing of sound pressure during turing on a lathe. The stability control methodology can select stable spindle speeds without knowing a prior knowledge of machine compliances and cutting dynamics. Reliability of the developed stability control methodology was verified through turing experiments on an engine lathe. Experimental results show that a microphone is an excellent sensor for chatter detection and control .

• Journal of Ocean Engineering and Technology
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• v.34 no.3
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• pp.227-236
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• 2020

Underwater acoustics, which is the domain that addresses phenomena related to the generation, propagation, and reception of sound waves in water, has been applied mainly in the research on the use of sound navigation and ranging (SONAR) systems for underwater communication, target detection, investigation of marine resources and environment mapping, and measurement and analysis of sound sources in water. The main objective of remote sensing based on underwater acoustics is to indirectly acquire information on underwater targets of interest using acoustic data. Meanwhile, highly advanced data-driven machine-learning techniques are being used in various ways in the processes of acquiring information from acoustic data. The related theoretical background is introduced in the first part of this paper (Yang et al., 2020). This paper reviews machine-learning applications in passive SONAR signal-processing tasks including target detection/identification and localization.