Topic
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.
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TL;DR: In this paper, the authors analyse the associations between urban morphology and noise complaints and find that the relationship between noise and urban morphology is weaker in high-density boroughs than in other boroughs.
17 citations
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07 Jun 2012
TL;DR: In this paper, the authors proposed a system for dynamically adjusting audio signals by applying a gain to the signal in a spectrally varying manner to compensate for ambient noise, such that the sound is perceived to be unchanged in volume and spectral composition by the listener.
Abstract: The present invention features systems for dynamically adjusting audio signals by applying a gain to the signal in a spectrally varying manner to compensate for ambient noise, such that the sound is perceived to be unchanged in volume and spectral composition by the listener. The system obtains a threshold elevation for each frequency component by analyzing the spectral composition of the ambient noise. This threshold elevation is then used by a psychoacoustic model of hearing to determine an appropriate gain adjustment for the corresponding frequency component of the source signal which will make that source signal perceived by the human ear to be just as loud as if the noise were not present.
17 citations
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01 Nov 2007TL;DR: Results demonstrate the superiority of the proposed NDLMS algorithm over conventional LMS algorithms in achieving much smaller steady-state excess mean square errors.
Abstract: LMS adaptive noise cancellers are often used to recover signal corrupted by additive noise. A major drawback of conventional LMS algorithms is that the excess mean-square errors increase linearly with the desired signal power. This results in degraded performance when the desired signal exhibits large power fluctuations. In this paper, a normalized difference LMS (NDLMS) algorithm is proposed to deal with the situation when the desired signal is strong, e.g., speech signals. Simulations were carried out using real speech signal with different noise power levels in both stationary and nonstationary noise environments. Results demonstrate the superiority of the proposed NDLMS algorithm over conventional LMS algorithms in achieving much smaller steady-state excess mean square errors.
17 citations
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TL;DR: In this paper, the authors evaluated the subjective annoyance of low frequency noise (LFN) combined with additional sound and found that LFN's subjective annoyance increased linearly with increasing SPL.
17 citations