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Noise

About: Noise is a research topic. Over the lifetime, 5111 publications have been published within this topic receiving 69407 citations. The topic is also known as: Мопсы танцуют под радио бандитов из сталкера 10 часов.


Papers
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PatentDOI
TL;DR: In this article, an engine noise simulating novelty device is provided including a speaker for audibly transmitting audio signals upon the receipt of an audio signal from the engine of a vehicle.
Abstract: An engine noise simulating novelty device is provided including a speaker for audibly transmitting audio signals upon the receipt thereof. Further included is a sound module connected to the speaker and a tachometer of a vehicle. The sound module is adapted to communicate audio signals with the speaker which represent a sound, wherein a frequency of the sound is varied with a change in the revolutions per minute of the engine of the vehicle, as indicated by the tachometer.

23 citations

Patent
02 Mar 2005
TL;DR: In this paper, the sound pressure level of the audio sound is minimally reduced so that the voice becomes equal to or lower than the maximum allowable level of sound pressure in an apparatus for improving voice clarity by controlling a gain of a voice.
Abstract: In an apparatus for improving voice clarity by controlling a gain of a voice based on a sound pressure level of the voice and a sound pressure level of noise, it is determined whether a sound pressure level of a gain-controlled voice exceeds a maximum allowable level. If the sound pressure level exceeding the maximum allowable level is caused by audio sound, a sound pressure level of the audio sound is reduced. The sound pressure level of the audio sound is minimally reduced so that the sound pressure level of the voice becomes equal to or lower than the maximum allowable level.

23 citations

Journal ArticleDOI
TL;DR: A deep learning method to automatically recognize events of interest in the context of audio surveillance (namely screams, broken glasses and gun shots) is proposed, which outperforms the existing methods in terms of recognition rate.
Abstract: Audio surveillance is gaining in the last years wide interest. This is due to the large number of situations in which this kind of systems can be used, either alone or combined with video-based algorithms. In this paper we propose a deep learning method to automatically recognize events of interest in the context of audio surveillance (namely screams, broken glasses and gun shots). The audio stream is represented by a gammatonegram image. We propose a 21-layer CNN to which we feed sections of the gammatonegram representation. At the output of this CNN there are units that correspond to the classes. We trained the CNN, called AReN, by taking advantage of a problem-driven data augmentation, which extends the training dataset with gammatonegram images extracted by sounds acquired with different signal to noise ratios. We experimented it with three datasets freely available, namely SESA, MIVIA Audio Events and MIVIA Road Events and we achieved 91.43%, 99.62% and 100% recognition rate, respectively. We compared our method with other state of the art methodologies based both on traditional machine learning methodologies and deep learning. The comparison confirms the effectiveness of the proposed approach, which outperforms the existing methods in terms of recognition rate. We experimentally prove that the proposed network is resilient to the noise, has the capability to significantly reduce the false positive rate and is able to generalize in different scenarios. Furthermore, AReN is able to process 5 audio frames per second on a standard CPU and, consequently, it is suitable for real audio surveillance applications.

23 citations

Journal ArticleDOI
TL;DR: Quiet Time was devised to promote adequate rest for the authors' vulnerable critical care population and to implement strategies that will lower the noise level, promote rest and healing, and increase patient satisfaction.
Abstract: To purchase electronic and print reprints, contact the American Association of Critical-Care Nurses, 101 Columbia, Aliso Viejo, CA 92656. Phone, (800) 809-2273; fax, (949) 362-2049; e-mail, reprints@aacn.org. setting. After exclusions, 10 articles remained to serve as a framework for our Quiet Time development. The literature review was presented at a unit-based shared governance meeting. A Quiet Time subcommittee was created (comprising nurses from day and night shifts), and members were designated as “Quiet Time champions.” It was decided by the nursing staff that Quiet Time would be most beneficial from 2 to 3 PM and 2 to 4 AM daily. Education and information about the new Quiet Time initiative was disseminated to physicians, physical therapists, and other potential persons who might be affected. Nurses were encouraged to advocate for the rescheduling of routine procedures that occurred during our designated Quiet Time when appropriate. All admitted patients and their families are educated about Quiet Time during unit orientation. Families are encouraged to bring in music and other items that will specifically relax their family member (eg, a special blanket, soothing music). Before Quiet Time periods, nurses, if appropriate, premedicate for pain, pretoilet, and reposition patients to ensure a period of uninterrupted rest. When Quiet Time begins, a one-on-one announcement is made to every Critical care units can be hectic, chaotic, and generally overstimulating. The interventions that critical care patients require can interrupt normal sleep/wake cycles, causing cognitive and physiological disturbances such as delirium and hemodynamic instability. Providing a healing environment can often be a challenge, one that the coronary care unit (CCU) at The Valley Hospital has confronted head on with our Quiet Time initiative. Quiet Time was devised to promote adequate rest for our vulnerable critical care population. Two blocks of time (2-3 PM and 2-4 AM) have been designated during which lights are dimmed, noise-reduction strategies are implemented, and procedures are minimized. Our main goal is to implement strategies that will lower the noise level, promote rest and healing, and increase patient satisfaction. Our stated objectives are as follows: 1. Identify common noise sources that interfere with a patient’s ability to rest. 2. Measure sound levels in the CCU to which patients are often exposed. 3. Minimize procedures and disruptions during the designated time frame. 4. Increase patient satisfaction. We began by using the CINAHL database to perform a literature review, searching for the key words “quiet time,” “noise,” and “intensive care unit” and excluding articles that were not relevant to the CCU

23 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20221
2021125
2020217
2019224
2018243
2017214