• Journal of Broadcast Engineering
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• v.19 no.6
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• pp.819-825
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• 2014

In this paper, monaural ambient component extraction algorithm based on nonnegative matrix factorization (NMF) is described. The ambience component extraction algorithm in this paper is developed for audio upmixing system; Recent researches have shown that they can enhance listener envelopment if the extracted ambient signal is applied into the multichannel audio upmixing system. However, the conventional method stores all of the audio signal and processes all at once, so it cannot be applied to streaming system and digital signal processor (DSP) system. In this paper, the ambient component extraction algorithm based on on-line nonnegative matrix factorization is developed and evaluated to solve the problem. As a result of analysis of the processed signal with spectral flatness measures in the experiment, it was shown that the developed system can extract the ambient signal similarly with the conventional batch process system.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.44 no.4
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• pp.423-430
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• 2011

The most satisfactory split-beam transducer for fish sizing maintains a wide bearing angle region for correct fish tracking without interference from side lobes and lower sensitivity to fish echoes outside of the main lobe region to correctly measure the angular location of free-swimming fishes in the sound beam. To evaluate the performance of an experimentally developed 50 kHz split-beam transducer, the angular location of a target was derived from the electrical phase difference between the resultant signals for the pair of transducer quadrants in the horizontal and vertical planes consisting of 32 transducer elements. The electrical phase difference was calculated by cross-spectral density analysis for the signals from the pair of receiving transducer quadrants, and the directivity correction factor for a developed split-beam transducer was estimated as the fourth-order polynomial of the off-axis beam angle for the angular location of the target. The experimental results demonstrate that the distance between the acoustic centers for the pair of receiving transducer quadrants can be controlled to less than one wavelength by optimization with amplitude-weighting transformers, and a smaller center spacing provides a range of greater angular location for tracking of a fish target. In particular, a side lobe level of -25.2 dB and an intercenter spacing of $0.96\lambda$($\lambda$= wavelength) obtained in this study suggest that the angular location of fish targets distributing within a range of approximately ${\pm}28^{\circ}$ without interference from side lobes can be measured.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.1
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• pp.70-75
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• 2007

In order to mitigate the problems in storage space and network bandwidth for the full CD quality audio with 44.1 kHz sampling rate, current existing digital audio is always restricted by sampling rate and bandwidth. This kind of restriction normally can be resolved by using low bit rate audio codec such as MP3, OGG, and AAC. However it suffers a major problem such as a loss of high frequency fidelity. This high frequency loss will reproduce only the band-limited low-frequency part of audio in the standard CD-quality audio. In general, the high frequency contents of audio have lots of information such as localization and ambient information, and bright nature of audio. The purpose of this paper is to implement on ARM platform system that can effectively estimate and compensate the missing high frequency contents of MP3 audio. From the experimental results with spectrum analysis and listening test, we confirm the superiority of the proposed algorithms for MP3 audio quality enhancement.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.429-438
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• 1997

Mechanical valve is one of the most widely used implantable artificial organs of which the reliability is so important that its failure means the death of patient. Therefore early noninvasive detection is essentially required, though mechanical valve failure with thrombosis is the most common. The objective of this paper is to detect the thrombosis formation by spectral analysis and neural network. Using microphone and amplifier, we measured the sound from the mechanical valve which is attached to the pneumatic ventricular assist device. The sound was sampled by A/D converter(DaqBook 100) and the periodogram is the main algorithm for obtaining spectrum. We made the thrombosis models using pellethane and silicon and they are thrombosis model on the valvular disk, around the sewing ring and fibrous tissue growth across the orifice of valve. The performance of the measurment system was tested firstly using 1 KHz sinusoidal wave. The measurement system detected well 1KHz spectrum as expected. The spectrum of normal and 5 kinds of thrombotic valve were obtained and primary and secondary peak appeared in each spectrum waveform. We find that the secondary peak changes according to the thrombosis model. So to distinguish the secondary peak of normal and thrombotic valve quantatively, 3 layer back propagation neural network, which contains 7, 000 input node, 20 hidden layer and 1 output was employed The trained neural network can distinguish normal and valve with more than 90% probability. As a conclusion, the noninvasive monitoring of implanted mechanical valve is possible by analysing the acoustical spectrum using neural network algorithm and this method will be applied to the performance evaluation of other implantable artificial organs.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.603-616
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• 2023

In this study, machine learning-based clustering and classification of residential noise in apartment buildings was conducted using frequency and temporal characteristics. First, a residential noise source dataset was constructed . The residential noise source dataset was consisted of floor impact, airborne, plumbing and equipment noise, environmental, and construction noise. The clustering of residential noise was performed by K-Means clustering method. For frequency characteristics, L_eq and L_max values were derived for 1/1 and 1/3 octave band for each sound source. For temporal characteristics, Leq values were derived at every 6 ms through sound pressure level analysis for 5 s. The number of k in K-Means clustering method was determined through the silhouette coefficient and elbow method. The clustering of residential noise source by frequency characteristic resulted in three clusters for both L_eq and L_max analysis. Temporal characteristic clustered residential noise source into 9 clusters for L_eq and 11 clusters for L_max. Clustering by frequency characteristic clustered according to the proportion of low frequency band. Then, to utilize the clustering results, the residential noise source was classified using three kinds of machine learning. The results of the residential noise classification showed the highest accuracy and f1-score for data labeled with L_eq values in 1/3 octave bands, and the highest accuracy and f1-score for classifying residential noise sources with an Artificial Neural Network (ANN) model using both frequency and temporal features, with 93 % accuracy and 92 % f1-score.