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Open accessProceedings ArticleDOI
07 Aug 2018
33 Citations
This paper brings IoT to active noise cancellation by combining wireless communication with acoustics.
It is shown that the problem of image noise cancellation can be characterized in terms of LMIs.
Proceedings ArticleDOI
Hsiang-Feng Chi, Shawn X. Gao, S.D. Soli 
30 May 1999
16 Citations
In this paper, a band-limited adaptive feedback cancellation algorithm for hearing aids is proposed.
Proceedings ArticleDOI
Gustav Tolt, Ivan Kalaykov 
02 Dec 2001
14 Citations
Test examples and comparisons with other image noise cancellation techniques show the advantages of the method.
Acoustic noise and echo cancellation using two adaptive filters and the least-mean-square (LMS) algorithm is the most effective technique of reducing acoustic noises and echoes in a hands-free telephone system.
This IM2 cancellation works well even though the actual IM2 components are far below the desired signal and thermal noise.
With a dominant noise source present and with the pilot wearing an oxygen facemask, we demonstrate that good (>10 dB) cancellation of the additive noise and little speech distortion can be achieved by having the reference microphone attached to the outside of the facemask and by updating the filter coefficients only during silence intervals.
In this paper, we propose a noise cancellation method for estimating a speciflc signal with the existence of background noise of non-Gaussian distribution.
We concluded that the Elizabethan collar did not have any effect on ambulatory ECG and canine movements and was effective to protect the recording apparatus.
It is shown that the performance of the new procedure for noise cancellation for multi-sensory signals is improved when compared to previously proposed methods.
The analysis and results on proposed technique for noise cancellation shows promising performance.
The quantitative and the qualitative measures used as the quality metrics demonstrated the ability of the proposed method for noise cancellation.
The obtained results indicate efficiency of the method in impulsive noise cancellation and improve the ability to detect a damage.
Book ChapterDOI
Gustav Tolt, Ivan Kalaykov 
03 Feb 2002
Comparisons with other image noise cancellation techniques show the advantages of the method.

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What are the pitch characteristics of children in speech recognition?
3 answers
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?
5 answers
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.
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3 answers
The abstracts provided do not contain any information about the current weather conditions.
How does inaccurate travel demand modeling forecasts affect transportation policy development or policy performance?
5 answers
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?
5 answers
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?
5 answers
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?
5 answers
Noise pollution has a significant impact on human health and the environment. It is considered an environmental pollutant that can cause both auditory and non-auditory effects on human health. The exposure to noise pollution can lead to hearing loss, tinnitus, psychological distress, sleep disturbances, hypertension, cardiovascular diseases, diabetes mellitus, headache, and pulmonary diseases. Noise pollution can also have negative effects on sensitive individuals such as autistic and elderly people. In addition to its impact on human health, noise pollution can also affect the environment by causing water, air, and soil pollution. It is important to address noise pollution through strategies and educational programs to reduce its potential negative effects on human health.
Does classroom-based oral activity have an effect on agitation in children with autism?
4 answers
Classroom-based oral activity has been found to have an effect on agitation in children with autism. Research has shown that the acoustical design of classrooms can impact the behavior of children with autism, including repetitive speech and motor movement. Additionally, a tablet-based application for activity schedules has been shown to improve the execution of classroom and communication routines in children with autism. Furthermore, alterations in the salivary microbiome have been linked to autism spectrum disorder (ASD), suggesting that disruptions in the oral microbiome may contribute to behavioral changes in children with ASD. Interventions such as modified Cognitive Behavior Therapy (CBT) and Functional Behavior Analysis-Applied Behavior Analysis (FBA/ABA) have also been found to reduce anxiety in children with high-functioning autism and Asperger Syndrome, indirectly impacting agitation levels.
Does audiovisual distraction affect attention?
5 answers
Audiovisual distraction does affect attention. Studies have shown that auditory distractors can interfere with visual search performance, leading to increased error rates and affecting the speed-accuracy tradeoff. However, the presence of a highly effective attentional mechanism can block auditory distraction and prevent interference with visual tasks. The processing of irrelevant multisensory information, including audiovisual distractors, is not influenced by higher-order cognition. Different types of auditory distraction, such as changing-state sound and auditory deviants, can disrupt short-term memory and attention, but their effects are not reduced by increased task load or perceptual task load. Non-informational speech spectrum noise as a distractor can enhance arousal levels and lead to faster information processing during cognitive tasks, but it may also result in more errors in updating items.
What are the different types of noise barriers that are available?
5 answers
There are several types of noise barriers available. One type is porous concrete barriers, which incorporate expanded clay as aggregate and have proven efficiency in reducing noise levels. These barriers can be used near roads or railways and have good insertion loss values when carefully located within the barrier. Another type is sound barriers with phase gradient surfaces, which feature a linear phase profile and work by steering the reflected sound downward. These barriers have been designed to eliminate the constructive interference of sound reflected from the other barrier and have application in environmental noise control. Additionally, there are sonic composites consisting of an array of acoustic scatterers embedded in air. These scatterers, made of functionally graded materials, lead to selective sound attenuation in certain frequency bands called band gaps. The simulation of these barriers confirms the existence of band gaps and multiple resonances at frequencies below the first band gap. Furthermore, noise barriers with attached caps of different shapes have been studied. These caps can significantly impact the diffracted sound energy and increase the desired global acoustic losses. Different cap shapes, such as cylinders, rectangles, trapezoids, and Y/T-shaped forms, have been considered, with Y-shaped caps being the most effective at increasing insertion losses without raising the height of the barrier.
What are signal-tonoise ratio differences in ERP?
3 answers
Signal-to-noise ratio (SNR) differences in ERP can vary depending on the specific context. In magnetic resonance imaging systems, reducing the bandwidth of received signals and adjusting the sampling time can increase the SNR. In the measurement of electrical signals, an adjustable meter is used to set the signal level, and a square wave is added to the signal for display on an oscilloscope. The adjusted square wave amplitude is then measured to determine the SNR. In the context of a dual-band simultaneous sampling measuring chain, the noise level is suppressed, resulting in a higher SNR compared to the original. In the evaluation of inflammatory activity in alpha-synucleinopathies, such as Parkinson's disease, NLR and PLR values are significantly higher compared to a control group, suggesting possible neuroinflammatory patterns. Overall, the SNR differences in ERP can be influenced by various factors and measurement techniques.