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How do I set the time in noise on Colorfit NAV?

Noise measurement

Word recognition

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Journal Article•DOI D1 dopamine receptors modulate cone ON bipolar cell Nav channels to control daily rhythms in photopic vision. Benjamin J. Smith, Patrice D. Côté, François Tremblay - Show less +2 more 02 Jan 2015-Chronobiology International 12 Citations	Taken together, these results suggest that circadian control of ON-CBC function contains an initial phase after approximately 18–30 h of dark adaptation, leading to substantial changes in b-wave amplitude after a relatively short time in free run which are dependent on D1R modulation of Nav channels.
Journal Article•DOI Robust voice activity detection algorithm for estimating noise spectrum Kyoung Ho Woo, Tae Young Yang, Kun Jung Park, Chungyong Lee - Show less +3 more 20 Jan 2000-Electronics Letters 157 Citations	Using the noise period we can reliably estimate the time-varying noise characteristics.
Open access•Journal Article•DOI Perceptual learning for speech in noise after application of binary time-frequency masks. Mahnaz Ahmadi, Vauna L. Gross, Donal G. Sinex - Show less +2 more 06 Mar 2013-Journal of the Acoustical Society of America 14 Citations	Ideal time-frequency (TF) masks can reject noise and improve the recognition of speech-noise mixtures.
Proceedings Article•DOI An Efficient Statistical Method for Image Noise Level Estimation Guangyong Chen, Fengyuan Zhu, Pheng-Ann Heng - Show less +2 more 07 Dec 2015 225 Citations	Specifically, our method outperforms existing state-of-the-art algorithms on estimating noise level with the least executing time in our experiments.
Journal Article•DOI Phase noise and timing jitter in oscillators with colored-noise sources Alper Demir 01 Dec 2002-IEEE Transactions on Circuits and Systems I-regular Papers 201 Citations	Our treatment of phase noise due to colored-noise sources is general, i. e., it is not specific to a particular type of colored-noise source.
Journal Article•DOI Traffic noise affects colouration but not calls in the European treefrog (Hyla arborea) Mathieu Troïanowski, Camille Condette, Nathalie Mondy, Adeline Dumet, Thierry Lengagne - Show less +4 more 01 Jan 2015-Behaviour 10 Citations	Our findings highlight for the first time the negative effect of traffic noise on colour signals.

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What are the pitch characteristics of children in speech recognition?

The pitch characteristics of children in speech recognition have been studied in several papers. Children's speech is found to have high values of pitch, abnormal spectrum, and well-marked high-frequency. The pitch of children's speech is generally higher than that of adults. The increase in pitch of children's speech leads to an increase in the dynamic range and variances of higher order coefficients of Mel frequency cepstral coefficient (MFCC) features. To address the pitch differences between adults and children, various techniques have been proposed, such as spectral smoothening, pitch reduction, and pitch-based spectrum normalization. These techniques aim to improve the performance of automatic speech recognition (ASR) systems trained on adults' speech for recognizing children's speech.How does external noise affect someone's memory?

External noise can have an impact on someone's memory. Studies have shown that increasing external noise does not affect the precision with which visual information can be recalled from working memory. However, meaningful external noise at low sound pressure levels can cause annoyance and decrease performance in mental tasks, such as short-term memory tasks. Additionally, external noise can lead to a shift in memory strategies employed by individuals. In the presence of noise, individuals with an external locus of control tend to decrease their use of higher-level semantic memory strategies and increase their use of lower-level perceptual strategies. Therefore, external noise can have negative effects on memory recall and the strategies used for memory tasks.What is the weather outside?

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Age has been found to have an impact on reading comprehension. Older adults may experience declines in certain component processes of comprehension, such as the ability to remember new text information, make inferences, access prior knowledge, and integrate prior knowledge with new information. However, it is important to note that some basic language processes, like reading comprehension, can remain preserved despite changes to brain integrity with aging. In the case of 10-year-old children, word reading skills (measured as access to orthographic representations) account for a modest amount of the variance in reading comprehension accuracy. However, when reading comprehension is conceptualized as rate (i.e., number of correct responses per minute), the correlation with word reading increases. Therefore, age can have different effects on reading comprehension depending on the specific age group being studied.How does inaccurate travel demand modeling forecasts affect transportation policy development or policy performance?

Inaccurate travel demand modeling forecasts can have significant implications for transportation policy development and policy performance. These forecasts, generated by AI-based models, may introduce prediction biases and fairness issues, potentially exacerbating social inequalities in decision-making processes. Limited studies have focused on addressing the fairness issues of these models, highlighting the need for fairness-aware travel demand forecasting methodologies. Additionally, research has shown that measured traffic is often lower than forecasted volumes, with a mean absolute deviation of 17% from the forecast. Factors such as road volume, functional class, time span, and the use of travel models can influence forecast accuracy. Furthermore, the presence of infrequently large prediction errors can impact the performance of demand-responsive public transport systems, highlighting the importance of accurate demand predictions for effective operation.What are the key challenges that acoustic textiles face in the coming years?

Acoustic textiles face several key challenges in the coming years. One challenge is to enhance their performance characteristics to be comparable to conventional acoustic materials such as glass fibre mats and polyurethane foams. Another challenge is to minimize the damage inflicted on the environment throughout the whole life cycle of the textile product. Additionally, the electrical performance of textile circuits woven or knitted within a textile matrix presents unusual constraints due to the textile manufacturing process and the properties of the fibers used. Signal attenuation and the ability to form reliable interconnections are major challenges for distributed sensors connected via an electronic fabric. Furthermore, the competitiveness of products such as cars and washing machines is being considered in terms of noise levels, making noise control an important challenge for acoustic textiles.What materials are used for making acoustic textiles?

Textile materials are widely used for making acoustic textiles. These materials include natural and synthetic fibers, recycled materials, and nanomaterials. Various manufacturing techniques are employed to produce textile products for acoustic insulation, such as fibrous mats, needlepunched nonwovens, and polymer composites made of nonwovens. The porosity of textile structures is crucial for sound absorption, and efforts have been made to produce thick textile structures with sufficient porosity. Additionally, the combination of different textile structures is used to counter low-frequency noises. The use of chemicals and fillers, as well as the development of hybrid absorbers, are also explored in the field of acoustic textiles. Overall, textile materials offer a cheaper, simpler, and effective alternative to conventional acoustic materials for noise control applications.What is the impact of noise pollution on human health and environment?

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The latest advances in noise cancellation technology include the use of an improved phase-generated carrier (PGC) demodulation technique for phase noise cancellation. This technique combines an auxiliary reference interferometer scheme with an ellipse fitting algorithm (EFA) to eliminate the effect of different phase modulation depths due to different optical path length differences (OPDs), significantly reducing harmonic distortion. Another advancement is the development of variants of the Filtered-x LMS (FxLMS) algorithm, such as the Feedback FxLMS (FB-FxLMS) algorithm, which provide an improved approach for noise cancellation in noisy environments. Additionally, there is a structure that includes an adaptive noise canceller circuit designed to suppress noise in a feedback sigma-delta modulator circuit and provide real-time tracking of a noise cancellation signal. Furthermore, systems and methods have been developed for compensating for mechanical acceleration at a reference oscillator, using an accelerometer and an adaptive weighting component to adjust filter weights based on a comparison of external signals and the oscillator output signal.