scholarly journals Toward acoustic noise type detection based on QQ plot statistics

2017 ◽  
Vol 30 (4) ◽  
pp. 571-584 ◽  
Author(s):  
Sanja Vujnovic ◽  
Aleksandra Marjanovic ◽  
Zeljko Djurovic ◽  
Predrag Tadic ◽  
Goran Kvascev
Keyword(s):  
State Estimation ◽  
Acoustic Signal ◽  
Acoustic Noise ◽  
Thermal Power ◽  
Noise Pollution ◽  
Detection Algorithm ◽  
Noise Detection ◽  
Statistical Parameters ◽  
Industrial Environment ◽  
Stochastic Signal

Fault detection and state estimation using acoustic signals is a procedure highly affected by ambient noise. This is particularly pronounced in an industrial environment where noise pollution is especially strong. In this paper a noise detection algorithm is proposed and implemented. This algorithm can identify the times in which the recorded acoustic signal is influenced by different types of noise in the form of unwanted impulse disturbance or speech contamination. The algorithm compares statistical parameters of the recordings by generating a series of QQ plots and then using an appropriate stochastic signal analysis tools like hypothesis testing. The main purpose of this algorithm is to eliminate noisy signals and to collect a set of noise free recordings which can then be used for state estimation. The application of these techniques in a real industrial environment is extremely complex because sound contamination usually tends to be intense and nonstationary. The solution described in this paper has been tested on a specific problem of acoustic signal isolation and noise detection of a coal grinding fan mill in thermal power plant in the presence of intense contaminating sound disturbances, mainly impulse disturbance and speech contamination.

scholarly journals Development of Wireless Sensor Device for Machine English Oral Pronunciation Noise Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yaning Zhu
Keyword(s):  
Closed Loop ◽  
Detection Algorithm ◽  
Training System ◽  
Circuit Board ◽  
Wireless Sensor ◽  
Noise Detection ◽  
Traditional System ◽  
Spoken English ◽  
Loop Design ◽  
Detection Technology

There is often noise in spoken machine English, which affects the accuracy of pronunciation. Therefore, how to accurately detect the noise in machine English spoken language and give standard spoken pronunciation is very important and meaningful. The traditional machine-oriented spoken English speech noise detection technology is limited to the improvement of software algorithm, mainly including speech enhancement technology and speech endpoint detection technology. Based on this, this paper will develop a wireless sensor network based on machine English oral pronunciation noise based on air and nonair conduction, reasonably design and configure air sensors, and nonair conduction sensors to deal with machine English oral pronunciation noise, so as to improve the naturalness and intelligibility of machine English speech. At the hardware level, this paper mainly optimizes the AD sampling, sensor matching layout, and internal hardware circuit board layout of the two types of sensors, so as to solve the compatibility problem between them and further reduce the hardware power consumption. In order to further verify or evaluate the performance of the machine spoken English speech noise detection sensor designed in this paper, a machine spoken English training system based on Android platform is designed. Compared with the traditional system, the training system can improve the intelligence of machine oriented oral English noise detection algorithm, so as to continuously improve the accuracy of system detection. The machine English pronunciation is adjusted and corrected by combining the data sensed by the sensor, so as to form a closed-loop design. The experimental results show that the wireless sensor sample proposed in this paper has obvious advantages in detecting the accuracy of machine English oral pronunciation, and its good closed-loop system is helpful to further improve the accuracy of machine English oral pronunciation.

scholarly journals ACOUSTIC NOISE POLLUTION FROM MARINE INDUSTRIAL ACTIVITIES: EXPOSURE AND IMPACTS

2018 ◽  
pp. 148-161 ◽  
Author(s):  
Halit Kuşku ◽  
Murat Yiğit ◽  
Sebahattin Ergün ◽  
Ümüt Yiğit ◽  
Nic Taylor
Keyword(s):  
Acoustic Noise ◽  
Noise Pollution ◽  
Industrial Activities

Experimental Study of Aerodynamic Noise From the Trailing Edge and Leading Edge of Blades in a Coaxial-Double Shaft Fan

2013 ◽  
Author(s):  
Jafar Madadnia ◽  
Mustafa Shekeb ◽  
Thimantha Ulluwishewa
Keyword(s):  
Wind Turbines ◽  
Acoustic Noise ◽  
Residential Building ◽  
Leading Edge ◽  
Noise Pollution ◽  
Aerodynamic Noise ◽  
Trailing Edge ◽  
Double Row ◽  
Positive Effects ◽  
Spacing Variation

Proactive acoustic noise control technologies in wind turbines and blowers have in recent years been the focus of intensive research to integrate wind turbines in residential building and to address public concerns on noise pollution. However efforts to understand the mechanics has been inconclusive, mainly due to the complexity and commercial confidentiality of the topic. The paper reports on the experimental investigation on two methods in controlling aerodynamic noise. A counter-rotating-double-row-turbine with variable gap/spacing (s) was designed, built and tested. Serrations were designed and attached on the leading edge and the trailing edge of the blades to proactively control aerodynamic noise. The model was operated in fan-mode and air velocity, shaft-revolution; electric-fan-power, acoustic noise amplitude (dB) and Centre frequency (CF in Hz) were measured for a number of spacing and serrations. Coefficients of Performance (COP), dB, CF were plotted against tip speed (TS). It was noticed that: • The double-shaft-fan has operated quieter than the single shaft fan especially as TS decreases. Acoustic noise (dB) dropped 20% at TS = 4m/s to less than 2% at TS = 10m/s. Efficiency and CF increased in the double-shaft fan as TS increased. Spacing variation between blade-rows had insignificant effect on the dB, Cf, and efficiency. • Serrations on single-shaft fan have also reduced dB (up to 10%), increased efficiency and CF with more positive effects with the serrations on the leading edge than the trailing edge. Serrations are more effective at higher TS range. • Serrations on a double-shaft fan with an optimum spacing, reduced acoustic noise (dB) only allow speeds [at TS <4m/s]. However minor improvement was noticed in efficiency or noise frequency.

scholarly journals A Digital Signal Processor Based Acoustic Sensor for Outdoor Noise Monitoring in Smart Cities

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 605 ◽  
Author(s):  
Juan Manuel López ◽  
Jesús Alonso ◽  
César Asensio ◽  
Ignacio Pavón ◽  
Luis Gascó ◽  
...  
Keyword(s):  
Acoustic Noise ◽  
Smart Cities ◽  
Monitoring Network ◽  
Noise Pollution ◽  
Sound Level ◽  
Noise Measurement ◽  
Acoustic Sensor ◽  
Measurement Instruments ◽  
Frequency Weighting

Presently, large cities have significant problems with noise pollution due to human activity. Transportation, economic activities, and leisure activities have an important impact on noise pollution. Acoustic noise monitoring must be done with equipment of high quality. Thus, long-term noise monitoring is a high-cost activity for administrations. For this reason, new alternative technological solutions are being used to reduce the costs of measurement instruments. This article presents a design for a versatile electronic device to measure outdoor noise. This device has been designed according to the technical standards for this type of instrument, which impose strict requirements on both the design and the quality of the device’s measurements. This instrument has been designed under the original equipment manufacturer (OEM) concept, so the microphone–electronics set can be used as a sensor that can be connected to any microprocessor-based device, and therefore can be easily attached to a monitoring network. To validate the instrument’s design, the device has been tested following the regulations of the calibration laboratories for sound level meters (SLM). These tests allowed us to evaluate the behavior of the electronics and the microphone, obtaining different results for these two elements. The results show that the electronics and algorithms implemented fully fit within the requirements of type 1 noise measurement instruments. However, the use of an electret microphone reduces the technical features of the designed instrument, which can only fully fit the requirements of type 2 noise measurement instruments. This situation shows that the microphone is a key element in this kind of instrument and an important element in the overall price. To test the instrument’s quality and show how it can be used for monitoring noise in smart wireless acoustic sensor networks, the designed equipment was connected to a commercial microprocessor board and inserted into the infrastructure of an existing outdoor monitoring network. This allowed us to deploy a low-cost sub-network in the city of Málaga (Spain) to analyze the noise of conflict areas due to high levels of leisure noise. The results obtained with this equipment are also shown. It has been verified that this equipment meets the similar requirements to those obtained for type 2 instruments for measuring outdoor noise. The designed equipment is a two-channel instrument, that simultaneously measures, in real time, 86 sound noise parameters for each channel, such as the equivalent continuous sound level (Leq) (with Z, C, and A frequency weighting), the peak level (with Z, C, and A frequency weighting), the maximum and minimum levels (with Z, C, and A frequency weighting), and the impulse, fast, and slow time weighting; seven percentiles (1%, 5%, 10%, 50%, 90%, 95%, and 99%); as well as continuous equivalent sound pressure levels in the one-third octave and octave frequency bands.

Identifying noisy features with the Pairwise Attribute Noise Detection Algorithm

2005 ◽  
Vol 9 (6) ◽  
pp. 589-602 ◽  
Author(s):  
Taghi M. Khoshgoftaar ◽  
Jason Van Hulse
Keyword(s):  
Detection Algorithm ◽  
Noise Detection ◽  
Attribute Noise

A Denoising Mixed Noise Method Based on Median Filter and Lifting Wavelet Technology

2013 ◽  
Vol 411-414 ◽  
pp. 1546-1551 ◽  
Author(s):  
Zhong Tao Qiao ◽  
Feng Qi Gao ◽  
Guang Long Wang ◽  
Liang Liang Chang
Keyword(s):  
Impulse Noise ◽  
Median Filter ◽  
Detection Algorithm ◽  
Noise Detection ◽  
Image Digitization ◽  
Mixed Noise ◽  
The Common ◽  
Lifting Wavelet ◽  
Impulse Noise Detection ◽  
Suppress Noise

In image digitization and transmission, images often suffer contamination inevitably. The noises in images often consist of Gaussian noise and impulse noise. The common denoising algorithms are capable of removing single one of them. In order to remove those two types of noise, a composite algorithm is proposed. Firstly, based on median filter, an impulse noise detection algorithm is used to filter impulse noise. Secondly, adaptive directional lifting wavelet (ADL) and normal lifting wavelet is combined to suppress noise from image signal and protect the texture edge from loss simultaneously. Meanwhile an improved half-soft threshold is used for normal lifting wavelet. At last, simulations show that this technology can suppress Gaussian and impulse noise in image efficiently．

Deficit in acoustic signal‐in‐noise detection in glycine receptor α3 subunit knockout mice

2017 ◽  
Vol 45 (4) ◽  
pp. 581-586 ◽  
Author(s):  
Konstantin Tziridis ◽  
Stefanie Buerbank ◽  
Volker Eulenburg ◽  
Julia Dlugaiczyk ◽  
Holger Schulze
Keyword(s):  
Acoustic Signal ◽  
Knockout Mice ◽  
Glycine Receptor ◽  
Noise Detection ◽  
Α3 Subunit
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