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: The two versions of the Mandarin Hearing In Noise Test (MHINT) are the first standardized Mandarin sentence speech intelligibility tests, and response variability within list was low, and inter-list reliability was high, indicating that consistent results can be obtained using any list.
Abstract: Objective: To develop two versions of the Mandarin Hearing In Noise Test (MHINT). These tests are adaptive tests that measure the reception threshold for sentences (RTSs) in quiet and in noise. The RTS is the presentation level at which half the sentences are correctly recognized. Design: Four studies were undertaken to (1) develop sentence materials, (2) equalize sentence difficulty, (3) create phonemically balanced sentence lists; and (4) evaluate within-list response variability, inter-list reliability, and produce normative data. A total of 137 native Mandarin (Putonghua) speaking subjects in Mainland China and 89 native Mandarin speakers in Taiwan participated. They had normal hearing thresholds at 25 dB HL or better. RTSs were measured under four headphone test conditions: Quiet, and in noise with noise originating from the 0 degree (Noise Front), 90 degrees to the right (Noise Right), and 90 degrees to the left (Noise Left). The speech originated from the front (0 degree) in all conditions. The noise level was fixed at 65 dBA, and the speech was varied adaptively to find the RTS. Results: Two versions of the test materials, consisting of 24, 20-sentence lists each in Mandarin spoken in the Mainland (the MHINT-M) and the dialect of Mandarin spoken in Taiwan (the MHINT-T), were created from two sets of 240 sentences containing 10 syllables per sentence. The mean Quiet RTS was 14.7 dBA, using the MHINT-M, and 19.4 dBA, using the MHINT-T. Using the MHINT-M, the mean RTS for Noise Front was -4.3 dB signal-to-noise ratio (SNR), -11.7 dB SNR for Noise Right, and -11.7 dB SNR for Noise Left. Using the MHINT-T, the Noise Front RTS was -4.0 dB SNR, -10.7 dB SNR for Noise Right, and -11.0 dB SNR for Noise Left. Results in noise are slightly better than those seen for the English HINT norms. Response variability within list was low, and inter-list reliability was high, indicating that consistent results can be obtained using any list. Confidence intervals are reported. Conclusions: The two versions of the MHINT are the first standardized Mandarin sentence speech intelligibility tests. Similar to other language versions of the HINT, the MHINT was developed using the same rationale as the English HINT, allowing norm-referenced results for the MHINT to be compared directly with results in other languages. The MHINT would benefit from further evaluation of its validity.
193 citations
TL;DR: On the basis of over 738,000 hourly spectral estimates computed from these stations' data, a robust noise model is developed that exhibits significant differences from previous models both in the normal mode and body wave bands.
Abstract: [1] It has been a decade since the last comprehensive model of ambient Earth noise was published (Peterson, 1993). Since then, observations of ambient Earth noise from the Incorporated Research Institutions for Seismology (IRIS) Global Seismographic Network (GSN) of widely distributed, similarly equipped, and well-calibrated stations have become available. The broad geographic sampling of this large data set and the ease of access to waveform data provided by the IRIS Data Management System facilitate analysis of global noise samples. We have analyzed data from the 118 GSN stations operating during the year July 2001 through June 2002. On the basis of over 738,000 hourly spectral estimates computed from these stations' data, we have developed a robust noise model that exhibits significant differences from previous models both in the normal mode and body wave bands. Our analysis technique has the advantage that we do not need to search for quiet periods but can include all data where the instruments are operating correctly.
189 citations
07 May 2001
TL;DR: In this paper, the authors present several mechanisms that enable effective spread-spectrum audio watermarking systems: prevention against detection desynchronization, cepstrum filtering, and chess watermarks.
Abstract: We present several mechanisms that enable effective spread-spectrum audio watermarking systems: prevention against detection desynchronization, cepstrum filtering, and chess watermarks. We have incorporated these techniques into a system capable of reliably detecting a watermark in an audio clip that has been modified using a composition of attacks that degrade the original audio characteristics well beyond the limit of acceptable quality. Such attacks include: fluctuating scaling in the time and frequency domain, compression, addition and multiplication of noise, resampling, requantization, normalization, filtering, and random cutting and pasting of signal samples.
182 citations
TL;DR: It is the temporal distortion rather than the spectral distortion of the low-frequency components that disrupts word identification, and a simulation of cochlear hearing loss had significantly less temporal distortion than was produced by jittering.
181 citations
TL;DR: Both sexes of zebra finches increased amplitude levels of vocalization in response to increased levels of noise, and similar results were obtained with humans.
180 citations