The sense of presence is crucial for evaluating audio-visual equipment. To clarify the effects of audio reproduction methods on the sense, two experiments were conducted under audio-only and audio-visual conditions. Twelve scenes were recorded with a high-definition video camera while their sounds were recorded using a dummy head. In the audio-only condition, the recorded audio signals were reproduced with headphones by five methods: binaural reproduction with and without the headphone transfer function calibration, binaural reproduction with the joint stereo coding, stereophonic reproduction in which head-related transfer functions from two loudspeakers to the both ears were convolved to the binaural signals, and diotic reproduction in which the left and right channel signals were superimposed. Twenty subjects evaluated each stimulus using a Likert scale. In the audio-visual condition, the same experiment was performed while video signals were reproduced with a 65-inch display. In the audio-only conditio...
{"title":"Modeling of the Effects of Audio Reproduction Methods on the Sense of Presence in Audio-Visual Content","authors":"Masashi Obinata, K. Ozawa, Yuichiro Kinoshita","doi":"10.1121/1.4709233","DOIUrl":"https://doi.org/10.1121/1.4709233","url":null,"abstract":"The sense of presence is crucial for evaluating audio-visual equipment. To clarify the effects of audio reproduction methods on the sense, two experiments were conducted under audio-only and audio-visual conditions. Twelve scenes were recorded with a high-definition video camera while their sounds were recorded using a dummy head. In the audio-only condition, the recorded audio signals were reproduced with headphones by five methods: binaural reproduction with and without the headphone transfer function calibration, binaural reproduction with the joint stereo coding, stereophonic reproduction in which head-related transfer functions from two loudspeakers to the both ears were convolved to the binaural signals, and diotic reproduction in which the left and right channel signals were superimposed. Twenty subjects evaluated each stimulus using a Likert scale. In the audio-visual condition, the same experiment was performed while video signals were reproduced with a 65-inch display. In the audio-only conditio...","PeriodicalId":262628,"journal":{"name":"Technical report of IEICE. EA","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124122059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wuttiwat Kongrattanaprasert, H. Nomura, T. Kamakura, K. Ueda
The detection of road surface conditions is an important process in efficient road management. In particular, in snowy seasons, prior information about the road conditions such as an icy state, helps road users or automobile drivers to obviate serious traffic accidents. This paper proposes a novel approach for automatically detecting the states of the road surface from tire noises of vehicles. The method is based on a wavelet transform analysis, artificial neural networks, and the mathematical theory of evidence. The proposed method employs the wavelet transform using multiresolution signal decomposition techniques. The proposed classification is carried out in sets of multiple neural networks using learning vector quantization networks. The outcomes of the networks are then integrated using the voting decision‐making scheme. It seems then feasible to detect passively and readily the states of the surface, i.e., as a rule of thumb, the dry, wet, snowy, and slushy state, automatically.
{"title":"Automatic Detection of Road Surface Conditions using Tire Noise from Vehicles","authors":"Wuttiwat Kongrattanaprasert, H. Nomura, T. Kamakura, K. Ueda","doi":"10.1121/1.4784506","DOIUrl":"https://doi.org/10.1121/1.4784506","url":null,"abstract":"The detection of road surface conditions is an important process in efficient road management. In particular, in snowy seasons, prior information about the road conditions such as an icy state, helps road users or automobile drivers to obviate serious traffic accidents. This paper proposes a novel approach for automatically detecting the states of the road surface from tire noises of vehicles. The method is based on a wavelet transform analysis, artificial neural networks, and the mathematical theory of evidence. The proposed method employs the wavelet transform using multiresolution signal decomposition techniques. The proposed classification is carried out in sets of multiple neural networks using learning vector quantization networks. The outcomes of the networks are then integrated using the voting decision‐making scheme. It seems then feasible to detect passively and readily the states of the surface, i.e., as a rule of thumb, the dry, wet, snowy, and slushy state, automatically.","PeriodicalId":262628,"journal":{"name":"Technical report of IEICE. EA","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123430818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Ishimitsu, K. Sakamoto, G. Onishi, T. Arai, T. Yoshimi, Y. Fujimoto, K. Kawasaki
In recent years, much attention has been directed at the sound design which designs various sound treated as noise, such as automobile acceleration sound and cleaner sound, because the point of view sound is a part of products, has permeated. This research considered the sound design and its evaluation about 11 kinds of the button sounds. First, the impression was extracted by the SD method and relevance with time frequency analysis was investigated. Moreover, we also confirmed that an impression changed, when the sound of a bad impression is processed into the sound of a good impression using adaptive control.
{"title":"A study of evaluating the button sounds","authors":"S. Ishimitsu, K. Sakamoto, G. Onishi, T. Arai, T. Yoshimi, Y. Fujimoto, K. Kawasaki","doi":"10.1121/1.2934134","DOIUrl":"https://doi.org/10.1121/1.2934134","url":null,"abstract":"In recent years, much attention has been directed at the sound design which designs various sound treated as noise, such as automobile acceleration sound and cleaner sound, because the point of view sound is a part of products, has permeated. This research considered the sound design and its evaluation about 11 kinds of the button sounds. First, the impression was extracted by the SD method and relevance with time frequency analysis was investigated. Moreover, we also confirmed that an impression changed, when the sound of a bad impression is processed into the sound of a good impression using adaptive control.","PeriodicalId":262628,"journal":{"name":"Technical report of IEICE. EA","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127088410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2005-05-13DOI: 10.1299/jsmecs.2005.43.411
S. Ishimitsu
{"title":"Study on Body-Conducted Speech Recognition for Support System of Marine Engine Operation","authors":"S. Ishimitsu","doi":"10.1299/jsmecs.2005.43.411","DOIUrl":"https://doi.org/10.1299/jsmecs.2005.43.411","url":null,"abstract":"","PeriodicalId":262628,"journal":{"name":"Technical report of IEICE. EA","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134037433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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