Car Cabin Interior Noise Classification Using Temporal Composite Features and Probabilistic Neural Network Model

2013 ◽  
Vol 471 ◽  
pp. 64-68 ◽  
Author(s):  
Paulraj Paulraj ◽  
Allan Andrew Melvin ◽  
Yaacob Sazali
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Objective Evaluation ◽  
Comfort Level ◽  
Stationary Condition ◽  
Continuous Exposure ◽  
Subjective Test ◽  
Noise Classification ◽  
The Relationship

Determination of vehicle comfort is important because continuous exposure to the noise and vibration leads to health problems for the driver and passengers. In this paper, a vehicle comfort level classification system has been proposed to detect the comfort level in cars using artificial neural network. A database consisting of sound samples obtained from 30 local cars is used. In the stationary condition, the sound pressure level is measured at 1300 RPM, 2000 RPM and 3000 RPM. In the moving condition, the sound is recorded while the car is moving at 30 km/h up to 110 km/h. Subjective test is conducted to find the Jurys evaluation for the specific sound sample. The correlation between the subjective and the objective evaluation is also tested. The relationship between the subjective results and the sound metrics is modelled using Probabilistic Neural Network. It is found from the research that the Temporal Composite Feature gives better classification accuracy for both stationary and moving condition model, 89.51% and 85.61% respectively.

scholarly journals The Relationship between the Circular Economy and Sustainable Waste Management in European Union

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Marcuta Liviu ◽  
Panait Razvan ◽  
Marcuta Alina
Keyword(s):  
European Union ◽  
Waste Management ◽  
Circular Economy ◽  
Comfort Level ◽  
Living Standards ◽  
The European Union ◽  
The Sustainable Development ◽  
European Policies ◽  
The Relationship

Modern life has contributed both to the increase of living standards, to the increase of the comfort level and to the development of the society, but also to the increase of the amount of waste that end up suffocating the planet and threatening the existence of present and future generations. Among the solutions that are sought and that are included in various programs and policies, the concept of circular economy is one that is increasingly discussed when talking about the sustainable development of society. The circular economy implies a reduction of the natural resources consumed due to both recycling and their fair consumption. At the E.U. level the foundations have been laid for policies aimed at waste management to ensure the application of the principles of the circular economy.Considering the importance that the quantification of the indicators for implementing the circular economy have on the elaboration of more efficient policies, but also on the determination of the degree of implementation of this concept, in this paper we intend to analyze the evolution of these indicators in 2010-2019, in the European Union using a customized version of the DPSIR model. Their analysis led us to the conclusion that although important steps have been taken towards the transition to the circular economy, there are still many aspects that need to be improved in order to achieve the proposed objectives through European policies.

Determination of Mechanical Parameters of Reservoir Landslide Based on Back Analysis Using Evolutionary Artificial Network

2012 ◽  
Vol 170-173 ◽  
pp. 729-734
Author(s):  
Fan Zhen Meng ◽  
Shao Jun Li ◽  
Zhen Hua Zhang
Keyword(s):  
Neural Network ◽  
Back Analysis ◽  
Mechanical Parameters ◽  
Three Gorges ◽  
Geological Materials ◽  
Highly Nonlinear ◽  
Artificial Network ◽  
The Relationship ◽  
Rock And Soil

Back analysis of displacement is an effective method for parameter recognition in geotechnical engineering. As rock and soil are complex geological materials, the relationship between the mechanical parameters of slope sliding mass and its displacement is incompletely quantified and highly nonlinear, but traditional back analysis of displacement has poor adaptability for this. So in this paper an integrating method of genetic algorithm, neural network and numerical analysis (GA-NN) is presented to carry out back analysis for mechanical parameters of slope sliding mass, and procedures to perform the intelligent back analysis are described in detail. Finally, this new method is applied and verified by a practical landslide in the reservoir area of Three Gorges, the results indicate the method is efficient for determination of mechanical parameters of sliding mass.

Deep neural network and noise classification-based speech enhancement

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740096 ◽  
Author(s):  
Wenhua Shi ◽  
Xiongwei Zhang ◽  
Xia Zou ◽  
Wei Han
Keyword(s):  
Neural Network ◽  
Speech Enhancement ◽  
Deep Neural Network ◽  
Gaussian Mixture ◽  
Noise Type ◽  
Noisy Observation ◽  
Enhancement Method ◽  
Noise Classification ◽  
Objective Speech Quality ◽  
The Relationship

In this paper, a speech enhancement method using noise classification and Deep Neural Network (DNN) was proposed. Gaussian mixture model (GMM) was employed to determine the noise type in speech-absent frames. DNN was used to model the relationship between noisy observation and clean speech. Once the noise type was determined, the corresponding DNN model was applied to enhance the noisy speech. GMM was trained with mel-frequency cepstrum coefficients (MFCC) and the parameters were estimated with an iterative expectation-maximization (EM) algorithm. Noise type was updated by spectrum entropy-based voice activity detection (VAD). Experimental results demonstrate that the proposed method could achieve better objective speech quality and smaller distortion under stationary and non-stationary conditions.

Objective evaluation of the rumbling sound in passenger cars based on an artificial neural network

2005 ◽  
Vol 219 (4) ◽  
pp. 457-469 ◽  
Author(s):  
Sang-Kwon Lee ◽  
Byung-Soo Kim ◽  
Dong-Chul Park
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Objective Evaluation ◽  
Sound Quality ◽  
Passenger Cars ◽  
Passenger Car ◽  
Artificial Neural ◽  
The Relationship

A rumbling sound is one of the most important sound qualities in a passenger car. In previous work, a method for objectively evaluating the rumbling sound was developed based on the principal rumble component. In the present paper, the rumbling sound was found to relate effectively not only to the principal rumble component but also to the loudness and roughness. The last two subjective parameters are sound metrics in psychoacoustics. The principal rumble component, roughness, and loudness were used as the sound metrics for the development of the rumbling index to evaluate the rumbling sound objectively. The relationship between the rumbling index and these sound metrics is identified by an artificial neural network. Interior sounds of 14 passenger cars were measured, and 21 passengers subjectively evaluated the rumbling sound qualities of these interior sounds. Through this research, it was found that the results of these evaluations and the output of a neural network have a high correlation. The rumbling index has been successfully applied to the objective evaluation of the rumbling sound quality of mass-produced passenger cars.

Probabilistic neural network for evaluating seismic liquefaction potential

2002 ◽  
Vol 39 (1) ◽  
pp. 219-232 ◽  
Author(s):  
Anthony TC Goh
Keyword(s):  
Neural Network ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Probabilistic Neural Network ◽  
Penetration Test ◽  
Cone Penetration ◽  
Liquefaction Potential ◽  
Seismic Liquefaction ◽  
The Relationship

Simplified techniques based on in situ testing methods are commonly used to assess seismic liquefaction potential. Many of these simplified methods are based on finding the liquefaction boundary separating two categories (the occurrence or non-occurrence of liquefaction) through the analysis of liquefaction case histories. As the liquefaction classification problem is highly nonlinear in nature, it is difficult to develop a comprehensive model taking into account all the independent variables, such as the seismic and soil properties, using conventional modeling techniques. Hence, in many of the conventional methods that have been proposed, simplified assumptions have been made. In this study, a probabilistic neural network (PNN) approach based on the Bayesian classifier method is used to evaluate seismic liquefaction potential based on actual field records. Two separate analyses are performed, one based on cone penetration test data and one based on shear wave velocity data. The PNN model effectively explores the relationship between the independent and dependent variables without any assumptions about the relationship between the various variables. Through the iterative presentation of the data (the learning phase), this study serves to demonstrate that the PNN can "discover" the intrinsic relationship between the seismic and soil parameters and the liquefaction potential. Comparisons indicate that the PNN models perform far better than the conventional methods in predicting the occurrence or non-occurrence of liquefaction.Key words: cone penetration test, neural networks, prediction, probabilistic neural network, sand, seismic liquefaction, shear wave velocity.

Sign in / Sign up

Export Citation Format

Share Document