• 성인간호학회지
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• 제13권2호
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• pp.209-222
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• 2001

The environment in the ICU leads to negative changes in a patient's usual sleep pattern and so contributes negatively to the patient's health condition as compared to patients in general wards. Therefore, it is thought that an important nursing intervention would be to identify the relation between noise and sleep patterns which play an important role in illness recovery. The purpose of the present study was to explore the relationship between noise in the ICU and the sleep pattern of patients admitted to the ICU. A descriptive correlation design was used to examine the relationship. Thirty-four subjects were recruited from a Medical ICU (MICU), Surgical ICU (SICU) and Coronary Care Unit (CCU) at a large university hospital in Suwon. Data were collected from September 28 to October 31 in 1999. In the present study, noise was categorized into noise level and patients' perception of noise. The objective noise level was measured using the A-Weighted Sound Level Meter. The patients' preception of noise was measured using a self-reported questionnaire developed by the researcher. Sleep patterns in this study includes both quantity and quality of sleep. These were measured using open ended questionnaires and the 'Korean Sleep Scale A' developed by Oh, Song, Kim(1998). The data was analyzed using the SPSS-WIN to test the research question, Pearson product moment correlation coefficient was run. Ancillary analysis were conducted with demographic variables to determine their relation to the main study variables. For the ancillary analysis, t-test and one-way ANOVAs were performed. The results of the present study are summerized as follows : 1. The total mean of objective noise level (10pm-6am) was 56.2dB. The means for night time noise level in individual ICUs for the SICU, MICU and CCU, were 58.7dB, 58.6dB and 48.3dB, respectively. The total mean for patients' noise perception was 42.8 out of a maximum possible score of 76. For item means of noise perception, the one ranked highest was "conversations between doctors and nurses" (3.2). The one ranked lowest was "noise from the radio" (1.2). Regarding the degree of perception for each type of noise source, the one ranked highest was "equipment noise" (2.6), the second was "conversation between medical staff" (2.4), the third was "conversation between patients, caregivers and visitors" (2.3), and the one ranked lowest was "environment noise" (1.8). 2. Looking at quantity of sleep of ICU patients, the mean nocturnal sleep time was found to be 4.9 hours. The total mean of sleep quality for ICU patients was 21.0 out of a maximum possible score of 40. 3. The relationship between perception of noise and quantity of sleep was statistically significant(r= - .41, p<.05). The relationship between perception of noise and quality of sleep was also statistically significant(r= - .47, p<.01). The results of the study indicate that personal perception of noise is related to sleep patterns. Therefore, it is suggested that nursing interventions be developed to reduce the degree of personal perception of noise and, thus, decrease sleep pattern disturbances in patients in the ICU.

• 대한음성학회지:말소리
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• 제64호
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• pp.105-119
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• 2007

Noise reduction technologies are indispensable to achieve acceptable speech quality in VoIP systems. This paper proposes a Wiener filter optimized to the estimated SNR of noisy speech for the noise reduction in VoIP environments. The proposed noise canceller is applied as a pre-processor before speech encoding. The performance of the proposed method is evaluated by the PESQ in various noisy conditions. In this paper, the proposed algorithm is applied to G.711, G.723.1, and G.729A which are all VoIP speech codecs. The PESQ results show that the performance of our proposed noise reduction scheme outperforms those of the noise suppression in the IS-127 EVRC and the ETSI standard for the advanced distributed speech recognition front-end.

• 대한음성학회지:말소리
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• 제48호
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• pp.155-173
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• 2003

This paper proposes a noise reduction-based speech detection method under telephone channel environments. We adopt the AURORA front-end noise reduction algorithm based on the two-stage mel-warped Wiener filter approach as a preprocessor for the frequency domain speech detector. The speech detector utilizes mel filter-bank based useful band energies as its feature parameters. The preprocessor firstly removes the adverse noise components on the incoming noisy speech signals and the speech detector at the next stage detects proper speech regions for the noise-reduced speech signals. Experimental results show that the proposed noise reduction-based speech detection method is very effective in improving not only the performance of the speech detector but also that of the subsequent speech recognizer.

• 성인간호학회지
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• 제20권3호
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• pp.365-374
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• 2008

Purpose: This study was conducted to evaluate quality of sleep and to assess the factors that influence quality of sleep in surgical ICU. Methods: The subject of the study were consisted 109 adult patients who admitted to surgical ICU. The data were collected from May 20 to December 10, 2007 by structured questionnaires. The data were analyzed with descriptive analysis, paired t-test, Pearson correlation coefficient and stepwise multiple regression. Results: The score of quality of sleep was 4.57 point. The main sleep disturbance factors related to quality of sleep in surgical ICU inpatient were sleep time, machinery alarm and noise(adjusted $R^2$=33.2). Conclusion: Based on the finding of this study, it is needed to develop a nursing intervention program that including to promote quality of sleep and to decrease machinery alarm and noise in surgical ICU.

• 대한음성학회지:말소리
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• 제63호
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• pp.125-137
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• 2007

Recently, many techniques have been proposed to improve the noise robustness for speaker verification. In this paper, we consider the feature recombination technique in multi-band approach. In the conventional feature recombination for speaker verification, to compute the likelihoods of speaker models or universal background model, whole feature components are used. This computation method is not effective in a view point of multi-band approach. To deal with non-effectiveness of the conventional feature recombination technique, we introduce a subband likelihood computation, and propose a modified feature recombination using subband likelihoods. In decision step of speaker verification system in noise environments, a few very low likelihood scores of a speaker model or universal background model cause speaker verification system to make wrong decision. To overcome this problem, a reliable feature selection method is proposed. The low likelihood scores of unreliable feature are substituted by likelihood scores of the adaptive noise model. In here, this adaptive noise model is estimated by maximum a posteriori adaptation technique using noise features directly obtained from noisy test speech. The proposed method using subband-based reliable feature selection obtains better performance than conventional feature recombination system. The error reduction rate is more than 31 % compared with the feature recombination-based speaker verification system.

• 한국직업건강간호학회지
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• 제10권2호
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• pp.174-182
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• 2001

This study was performed to find out the differences between noise levels of hospital wards and the nurses efforts for noise management in some general hospitals. The hospital wards selected were the intensive care unit(ICU), the emergency room(ER), the nursery room(NR), the internal medicine(IM), the general surgery(GS) among the 5 general hospitals located in Seoul. The data were collected from August 3 to September 13, 1999 through questionnaire survey and noise measurement in each nursing station of hospital wards. Data analysis was done by SPSS 8.0 package among the 305 questionnaires and 24 hours monitored noise levels. Frequency, Chi-square and ANOVA test were used. The study results were as belows: 1. The noise level measured by 24 hours monitoring survey were exceeded on the standard limit in all the hospital wards. Data also showed that noise levels were significantly different in each ward among the three shifts working duties. 2. The subjects were all female nurses. They were mostly working in the ICU ward(28.9%). They were 26~30 years old (43.9%), junior college graduates(57.0%), working for 1~5 years(55.1%) as staff-nurse(85.6%). There were no significant differences between hospital wards and general characteristics of nurses. 3. The noise levels perceived by nurses were regarded as 'Highly noisy'(56.4%), especially during the 11:30 and 15:30 (30.2%) o'clock. Data also showed that noise education was not ever given to nurses(89.9%). Nurses also responded that they hardly put an effort to reduce noise level(54.8%). However, there were significant differences between wards and noisy working time, experience of noise education and level of effort for noise reduction. 4. Nurses also perceived the ventilator alarm and EKG-alarm as the most disturbing sounds in the ICU, human voice and telephone ringing in the ER, human voice and EKG-alarming in the NR, human voices and telephone ringing in IM and GS both wards respectively in order. There were significant differences between hospital wards and noise making factors. 5. Nurses were shown that they regarded highly 'Sound reduction of the human voice', 'Careful handling on medical instruments', and 'Immediate appliances on alarming materials' as the practical method for noise management. There were significant differences between hospital wards and behavioral practical efforts for noise management. According to that results, the statistical differences were shown in the 24 hour monitored noise levels in each ward. Also, nurses perceived the noise severity differently and they approached variously on the practical efforts for noise reduction in each ward. Thus, author thinks that concrete and systematic endeavor will be necessary for noise reduction and management in hospitals for better working and healing environment for both of patients and staffs.

• 대한음성학회지:말소리
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• 제66호
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• pp.73-86
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• 2008

Beamforming is one of the spatial filtering techniques which extract only desired signals from noisy environments using microphone arrays. Fixed beamforming is a simple concept and easy to implement. However, it does not show good performance in real noisy conditions. As an adaptive beamforming, Frost algorithm can be a good candidate. It uses the concept of the linearly constrained minimum variance (LCMV) algorithm. The difference between the Frost and the LCMV algorithm is the error correction scheme which is very effective feature in the aspect of performance. In this paper, as quadrature mirror filtering (QMF)-based filterbank is utilized as the pre-processing of the Frost beamformning, the filter length and the learning rate of each band is optimized to improve the performance. The performance is measured by the signal-to-noise ratio (SNR) and the Bark's scale spectral distortion (BSD).

• Journal of Audiology & Otology
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• 제23권2호
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• pp.76-82
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• 2019

Background and Objectives: Noise levels and room acoustic parameters at a tertiary referral hospital, Seoul National University Hospital (SNUH) in Korea, are investigated. Materials and Methods: Through a questionnaire, acoustically problematic rooms are identified. Noise levels in emergency rooms (ERs) and intensive care units (ICUs) are measured over about three days. Acoustically critical and problematic rooms in the otolaryngology department are measured including examination rooms, operating rooms, nurse stations, receptions, and patient rooms. Results: The A-weighted equivalent noise level, L_Aeq, ranges from 54 to 56 dBA, which is at least 10 dB lower than the noise levels of 65 to 73 dBA measured in American ERs. In an ICU, the noise level for the first night was 66 dBA, which came down to 56 dBA for the next day. The noise levels during three different ear surgeries vary from 57 to 62 dBA, depending on the use of surgical drills and suctions. The noise levels in a patient room is found to be 47 dBA, while the nurse stations and the receptions have high noise levels up to 64 dBA. The reverberation times in an operation room, examination room, and single patient room are found to be below 0.6 s. Conclusions: At SNUH, the nurse stations and receptions were found to be quite noisy. The ERs were quieter than in the previous studies. The measured reverberation times seemed low enough but some other nurse stations and examination rooms were not satisfactory according to the questionnaire.

• 대한청각학회지
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• 제23권2호
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• pp.76-82
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• 2019

Background and Objectives: Noise levels and room acoustic parameters at a tertiary referral hospital, Seoul National University Hospital (SNUH) in Korea, are investigated. Materials and Methods: Through a questionnaire, acoustically problematic rooms are identified. Noise levels in emergency rooms (ERs) and intensive care units (ICUs) are measured over about three days. Acoustically critical and problematic rooms in the otolaryngology department are measured including examination rooms, operating rooms, nurse stations, receptions, and patient rooms. Results: The A-weighted equivalent noise level, L_Aeq, ranges from 54 to 56 dBA, which is at least 10 dB lower than the noise levels of 65 to 73 dBA measured in American ERs. In an ICU, the noise level for the first night was 66 dBA, which came down to 56 dBA for the next day. The noise levels during three different ear surgeries vary from 57 to 62 dBA, depending on the use of surgical drills and suctions. The noise levels in a patient room is found to be 47 dBA, while the nurse stations and the receptions have high noise levels up to 64 dBA. The reverberation times in an operation room, examination room, and single patient room are found to be below 0.6 s. Conclusions: At SNUH, the nurse stations and receptions were found to be quite noisy. The ERs were quieter than in the previous studies. The measured reverberation times seemed low enough but some other nurse stations and examination rooms were not satisfactory according to the questionnaire.

• 대한음성학회지:말소리
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• 제57호
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• pp.85-97
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• 2006

This paper suggests an algorithm that can estimate the direction of the sound source in real time. The algorithm uses the time difference and sound intensity information among the recorded sound source by four microphones. Also, to deal with noise of robot itself, the Kalman filter is implemented. The proposed method can take shorter execution time than that of an existing algorithm to fit the real-time service robot. Also, using the Kalman filter, signal ratio relative to background noise, SNR, is approximately improved to 8 dB. And the estimation result of azimuth shows relatively small error within the range of ${\pm}7$ degree.