@article{(Open Science Index):https://publications.waset.org/pdf/10002657,
	  title     = {Neural Networks-Based Acoustic Annoyance Model for Laptop Hard Disk Drive},
	  author    = {Yi Chao Ma and  Cheng Siong Chin and  Wai Lok Woo},
	  country	= {},
	  institution	= {},
	  abstract     = {Since the last decade, there has been a rapid growth in
digital multimedia, such as high-resolution media files and threedimentional
movies. Hence, there is a need for large digital storage
such as Hard Disk Drive (HDD). As such, users expect to have a
quieter HDD in their laptop. In this paper, a jury test has been
conducted on a group of 34 people where 17 of them are students
who are the potential consumer, and the remaining are engineers who
know the HDD. A total 13 HDD sound samples have been selected
from over hundred HDD noise recordings. These samples are
selected based on an agreed subjective feeling. The samples are
played to the participants using head acoustic playback system, which
enabled them to experience as similar as possible the same
environment as have been recorded. Analysis has been conducted and
the obtained results have indicated different group has different
perception over the noises. Two neural network-based acoustic
annoyance models are established based on back propagation neural
network. Four psychoacoustic metrics, loudness, sharpness,
roughness and fluctuation strength, are used as the input of the
model, and the subjective evaluation results are taken as the output.
The developed models are reasonably accurate in simulating both
training and test samples.},
	    journal   = {International Journal of Electronics and Communication Engineering},
	  volume    = {9},
	  number    = {8},
	  year      = {2015},
	  pages     = {964 - 968},
	  ee        = {https://publications.waset.org/pdf/10002657},
	  url   	= {https://publications.waset.org/vol/104},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 104, 2015},
	}