Artificial neural network based model for the crispness impression of the potato chip Sound

Research output: Contribution to conferencesPaperContributedpeer-review


Sound design and quality are becoming increasingly important for the food industry. The sounds which occur during biting the food or the packaging based sounds are designed to convey the product related information to the customer. Particularly such kind of sounds are important regarding the brand design. Previous studies noticed that the chewing sounds can influence the perceived food crispness. The aim of this study to predict the perceived crispness of the chips sounds using artificial neural networks (ANN). The chip bite sound has very impulsive type complex character. In a listening test, the recordings of the sound of 5 chips and filtered variations of the recordings were presented to the subjects and they evaluated the perceived crispness of the chip bite sounds. Psychoacoustical parameters which are based on temporal and spectral characteristics of the chip bite sounds were used as input parameters for the developed ANN. The results of the study was compared with previous food sound design studies.


Original languageEnglish
Publication statusPublished - 2017


Title24th International Congress on Sound and Vibration, ICSV 2017
Duration23 - 27 July 2017
CountryUnited Kingdom

External IDs

ORCID /0000-0002-0803-8818/work/142257063


ASJC Scopus subject areas


  • Crispness, Food sounds, Multisensory perception, Sound design, Sound quality