EVALUATION OF NONSTATIONARY VEHICLE PASSING LOUDNESS BASED ON AN ANTINOISE WAVELET PRE-PROCESSING NEURAL NETWORK MODEL

2009 ◽  
Vol 07 (04) ◽  
pp. 459-480 ◽  
Author(s):  
Y. S. WANG ◽  
C.-M. LEE
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Neural Network Model ◽  
Quality Evaluation ◽  
Sound Quality ◽  
Point Model ◽  
Vehicle Noise ◽  
A New Technique ◽  
Loudness Model

A new technique for sound loudness evaluation, the so-called antinoise wavelet pre-processing neural network (ANWT-NN) model, is presented in this paper. Based on passing vehicle noise, the ANWT-NN loudness model combines the techniques of wavelet analysis and neural network regression and classification. A wavelet-based, 21-point model for vehicle noise feature extraction is established. Verification shows that the trained ANWT-NN models are more accurate and effective than the WT-NN models for sound quality evaluation of nonstationary vehicle noises. The newly proposed ANWT-NN model can be applied to both the stationary and nonstationary sound signals and even to the transient ones. The ANWT-NN technique is suggested not only for the prediction, classification, and comparison of the sound quality of passing vehicle noise, but also for applications in other sound-related engineering fields, in place of the conventional psychoacoustical models.

Sound Quality Evaluation of the Interior Noise of Pure Electric Vehicle Based on Neural Network Model

2017 ◽  
Vol 64 (12) ◽  
pp. 9442-9450 ◽  
Author(s):  
Conggan Ma ◽  
Chaoyi Chen ◽  
Qinghe Liu ◽  
Haibo Gao ◽  
Qing Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Electric Vehicle ◽  
Network Model ◽  
Neural Network Model ◽  
Quality Evaluation ◽  
Sound Quality ◽  
Interior Noise

Sound Quality Evaluation Control of Car Interior Noise

2013 ◽  
Vol 415 ◽  
pp. 569-573 ◽  
Author(s):  
Fang Li ◽  
Yan Yan Zuo
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Quality Evaluation ◽  
Subjective Evaluation ◽  
Sound Quality ◽  
Interior Noise ◽  
Bp Neural Network Model

According to the complexity and non-linear characteristics of car interior sound quality evaluation, the technology of BP neural network is used in sound quality evaluation. The interior noise samples from actual cars were obtained by road experiment. The subjective evaluation test of sound quality annoyance was carried out. Meanwhile, several objective psycho-acoustical parameters of these samples were calculated. The sound quality prediction model of vehicle interior noise was established based on BP neural network. Annoyance of samples was obtained by means of the prediction model and the results were compared with that obtained by multiple liner regression prediction model. The results indicate that the prediction results from BP neural network model were close to the measured values. The BP neural network model was more effective than multiple liner regression model, and it can be used effectively to the evaluation of modern car noise.

scholarly journals Contribution to image restoration using a neural network model

2002 ◽  
Vol Volume 1, 2002 ◽  
Author(s):  
Karim Achour ◽  
Nadia Zenati ◽  
Oualid Djekoune
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Network Model ◽  
Neural Network Model ◽  
Hopfield Neural Network ◽  
Optimization Method ◽  
Nous Proposons ◽  
International Audience ◽  
High Level

International audience The reduction of the blur and the noise is an important task in image processing. Indeed, these two types of degradation are some undesirable components during some high level treatments. In this paper, we propose an optimization method based on neural network model for the regularized image restoration. We used in this application a modified Hopfield neural network. We propose two algorithms using the modified Hopfield neural network with two updating modes : the algorithm with a sequential updates and the algorithm with the n-simultaneous updates. The quality of the obtained result attests the efficiency of the proposed method when applied on several images degraded with blur and noise. La réduction du bruit et du flou est une tâche très importante en traitement d'images. En effet, ces deux types de dégradations sont des composantes indésirables lors des traitements de haut niveau. Dans cet article, nous proposons une méthode d'optimisation basée sur les réseaux de neurones pour résoudre le problème de restauration d'images floues-bruitées. Le réseau de neurones utilisé est le réseau de « Hopfield ». Nous proposons deux algorithmes utilisant deux modes de mise à jour: Un algorithme avec un mode de mise à jour séquentiel et un algorithme avec un mode de mise à jour n-simultanée. L'efficacité de la méthode mise en œuvre a été testée sur divers types d'images dégradées.

An Insect Song Signal Feature Extraction Method Based on the Wavelet Packet Analysis

2013 ◽  
Vol 423-426 ◽  
pp. 2614-2617
Author(s):  
Jun Xie ◽  
Hong Wei Wang ◽  
Mei Zhao ◽  
Kai Yu Yang
Keyword(s):  
Feature Extraction ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Wavelet Packet ◽  
Correlation Coefficients ◽  
Sound Quality ◽  
Signal Frequency ◽  
Feature Extraction Method ◽  
Frequency Decomposition ◽  
Cricket Song

The wavelet packet decomposition method was used to two common insects song signal. Frequency decomposition and feature extraction were made, the feature vectors, eigenvalues and the sound quality evaluation parameter vectors were constructed, then the correlation analysis calculation were made between the eigenvalues and the sound quality evaluation parameter vectors. The results show that the correlation coefficients are good, the average correlation coefficients of cricket and grasshopper song signals are 0.8875 and 0.6942, the results of cricket is much better than grasshopper, it proved that the proposed algorithm is more suitable for cricket song signals analysis, a new and effective sound quality evaluation method for typical insect with friction sound mechanism is provided.

A Study on the Sound Quality Evaluation Model of Mechanical Air-Cleaners

2009 ◽  
Vol 131 (3) ◽  
Author(s):  
Jeong-Guon Ih ◽  
Su-Won Jang ◽  
Cheol-Ho Jeong ◽  
Youn-Young Jeung
Keyword(s):  
High Frequency ◽  
Quality Evaluation ◽  
Principal Component ◽  
Sound Quality ◽  
Subjective Feeling ◽  
Subjective Data ◽  
Frequency Bands ◽  
Air Cleaner ◽  
Air Cleaners

In operating the air-cleaner for a long time, people in a quiet enclosed space expect low sound at low operational levels for a routine cleaning of air. However, in the condition of high operational levels of the cleaner, a powerful yet nonannoying sound is desired, which is connected to a feeling of an immediate cleaning of pollutants. In this context, it is important to evaluate and design the air-cleaner noise to satisfy such contradictory expectations from the customers. In this study, a model for evaluating the sound quality of air-cleaners of mechanical type was developed based on objective and subjective analyses. Sound signals from various air-cleaners were recorded and they were edited by increasing or decreasing the loudness at three wide specific-loudness bands: 20–400 Hz (0–3.8 barks), 400–1250 Hz (3.8–10 barks), and 1.25–12.5 kHz bands (10–22.8 barks). Subjective tests using the edited sounds were conducted by the semantic differential method (SDM) and the method of successive intervals (MSI). SDM tests for seven adjective pairs were conducted to find the relation between subjective feeling and frequency bands. Two major feelings, performance and annoyance, were factored out from the principal component analysis. We found that the performance feeling was related to both low and high frequency bands, whereas the annoyance feeling was related to high frequency bands. MSI tests using the seven scales were conducted to derive the sound quality index to express the severity of each perceptive descriptor. Annoyance and performance indices of air-cleaners were modeled from the subjective responses of the juries and the measured sound quality metrics: loudness, sharpness, roughness, and fluctuation strength. The multiple regression method was employed to generate sound quality evaluation models. Using the developed indices, sound quality of the measured data was evaluated and compared with the subjective data. The difference between predicted and tested scores was less than 0.5 points.

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