Influence of visual stimuli in the sound quality evaluation of loudspeaker systems

2006 ◽  
Vol 120 (5) ◽  
pp. 3342-3342
Author(s):  
Alex Karandreas ◽  
Flemming Christensen
Keyword(s):  
Quality Evaluation ◽  
Visual Stimuli ◽  
Sound Quality

scholarly journals Structural Design of Golf Club Based on Sound Quality Evaluation

2012 ◽  
Vol 78 (790) ◽  
pp. 2006-2014 ◽  
Author(s):  
Kohei FURUYA ◽  
Kazuaki TAKAGI ◽  
Nobuyuki OKUBO ◽  
Goro HISAMATSU ◽  
Takeshi TOI
Keyword(s):  
Structural Design ◽  
Quality Evaluation ◽  
Sound Quality ◽  
Golf Club

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.

Sound Design of Vacuum Cleaner Based on Sound Quality Evaluation

2003 ◽  
Vol 2003.13 (0) ◽  
pp. 71-72
Author(s):  
Susumu FUJIWARA ◽  
Daisuke SAKAI ◽  
Akihiro IWAHARA ◽  
Takeshi TOI
Keyword(s):  
Quality Evaluation ◽  
Sound Quality ◽  
Sound Design ◽  
Vacuum Cleaner
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