{"title":"研究场景视频对基于脑电图的乘用车内声音评估的影响","authors":"Liping Xie, Zhien Liu, Yi Sun, Yawei Zhu","doi":"10.1007/s12559-024-10303-2","DOIUrl":null,"url":null,"abstract":"<p>The evaluation of automobile sound quality is an important research topic in the interior sound design of passenger car, and the accurate and effective evaluation methods are required for the determination of the acoustic targets in automobile development. However, there are some deficiencies in the existing evaluation studies of automobile sound quality. (1) Most of subjective evaluations only considered the auditory perception, which is easy to be achieved but does not fully reflect the impacts of sound on participants; (2) similarly, most of the existing subjective evaluations only considered the inherent properties of sounds, such as physical and psychoacoustic parameters, which make it difficult to reflect the complex relationship between the sound and the subjective perception of the evaluators; (3) the construction of evaluation models only from physical and psychoacoustic perspectives does not provide a comprehensive analysis of the real subjective emotions of the participants. Therefore, to alleviate the above flaws, the auditory and visual perceptions are combined to explore the inference of scene video on the evaluation of sound quality, and the EEG signal is introduced as a physiological acoustic index to evaluate the sound quality; simultaneously, an Elman neural network model is constructed to predict the powerful sound quality combined with the proposed indexes of physical acoustics, psychoacoustics, and physiological acoustics. The results show that evaluation results of sound quality combined with scene videos better reflect the subjective perceptions of participants. The proposed objective evaluation indexes of physical, psychoacoustic, and physiological acoustic contribute to mapping the subjective results of the powerful sound quality, and the constructed Elman model outperforms the traditional back propagation (BP) and support vector machine (SVM) models. The analysis method proposed in this paper can be better applied in the field of automotive sound design, providing a clear guideline for the evaluation and optimization of automotive sound quality in the future.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Investigating the Influence of Scene Video on EEG-Based Evaluation of Interior Sound in Passenger Cars\",\"authors\":\"Liping Xie, Zhien Liu, Yi Sun, Yawei Zhu\",\"doi\":\"10.1007/s12559-024-10303-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The evaluation of automobile sound quality is an important research topic in the interior sound design of passenger car, and the accurate and effective evaluation methods are required for the determination of the acoustic targets in automobile development. However, there are some deficiencies in the existing evaluation studies of automobile sound quality. (1) Most of subjective evaluations only considered the auditory perception, which is easy to be achieved but does not fully reflect the impacts of sound on participants; (2) similarly, most of the existing subjective evaluations only considered the inherent properties of sounds, such as physical and psychoacoustic parameters, which make it difficult to reflect the complex relationship between the sound and the subjective perception of the evaluators; (3) the construction of evaluation models only from physical and psychoacoustic perspectives does not provide a comprehensive analysis of the real subjective emotions of the participants. Therefore, to alleviate the above flaws, the auditory and visual perceptions are combined to explore the inference of scene video on the evaluation of sound quality, and the EEG signal is introduced as a physiological acoustic index to evaluate the sound quality; simultaneously, an Elman neural network model is constructed to predict the powerful sound quality combined with the proposed indexes of physical acoustics, psychoacoustics, and physiological acoustics. The results show that evaluation results of sound quality combined with scene videos better reflect the subjective perceptions of participants. The proposed objective evaluation indexes of physical, psychoacoustic, and physiological acoustic contribute to mapping the subjective results of the powerful sound quality, and the constructed Elman model outperforms the traditional back propagation (BP) and support vector machine (SVM) models. The analysis method proposed in this paper can be better applied in the field of automotive sound design, providing a clear guideline for the evaluation and optimization of automotive sound quality in the future.</p>\",\"PeriodicalId\":51243,\"journal\":{\"name\":\"Cognitive Computation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-05-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cognitive Computation\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s12559-024-10303-2\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cognitive Computation","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s12559-024-10303-2","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Investigating the Influence of Scene Video on EEG-Based Evaluation of Interior Sound in Passenger Cars
The evaluation of automobile sound quality is an important research topic in the interior sound design of passenger car, and the accurate and effective evaluation methods are required for the determination of the acoustic targets in automobile development. However, there are some deficiencies in the existing evaluation studies of automobile sound quality. (1) Most of subjective evaluations only considered the auditory perception, which is easy to be achieved but does not fully reflect the impacts of sound on participants; (2) similarly, most of the existing subjective evaluations only considered the inherent properties of sounds, such as physical and psychoacoustic parameters, which make it difficult to reflect the complex relationship between the sound and the subjective perception of the evaluators; (3) the construction of evaluation models only from physical and psychoacoustic perspectives does not provide a comprehensive analysis of the real subjective emotions of the participants. Therefore, to alleviate the above flaws, the auditory and visual perceptions are combined to explore the inference of scene video on the evaluation of sound quality, and the EEG signal is introduced as a physiological acoustic index to evaluate the sound quality; simultaneously, an Elman neural network model is constructed to predict the powerful sound quality combined with the proposed indexes of physical acoustics, psychoacoustics, and physiological acoustics. The results show that evaluation results of sound quality combined with scene videos better reflect the subjective perceptions of participants. The proposed objective evaluation indexes of physical, psychoacoustic, and physiological acoustic contribute to mapping the subjective results of the powerful sound quality, and the constructed Elman model outperforms the traditional back propagation (BP) and support vector machine (SVM) models. The analysis method proposed in this paper can be better applied in the field of automotive sound design, providing a clear guideline for the evaluation and optimization of automotive sound quality in the future.
期刊介绍:
Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.