{"title":"机器聆听:与ART的声学接口","authors":"Benjamin D. Smith, Guy E. Garnett","doi":"10.1145/2166966.2167021","DOIUrl":null,"url":null,"abstract":"Recent developments in machine listening present opportunities for innovative new paradigms for computer-human interaction. Voice recognition systems demonstrate a typical approach that conforms to event oriented control models. However, acoustic sound is continuous, and highly dimensional, presenting a rich medium for computer interaction. Unsupervised machine learning models present great potential for real-time machine listening and understanding of audio and sound data. We propose a method for harnessing unsupervised machine learning algorithms, Adaptive Resonance Theory specifically, in order to inform machine listening, build musical context information, and drive real-time interactive performance systems. We present the design and evaluation of this model leveraging the expertise of trained, improvising musicians.","PeriodicalId":87287,"journal":{"name":"IUI. International Conference on Intelligent User Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Machine listening: acoustic interface with ART\",\"authors\":\"Benjamin D. Smith, Guy E. Garnett\",\"doi\":\"10.1145/2166966.2167021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recent developments in machine listening present opportunities for innovative new paradigms for computer-human interaction. Voice recognition systems demonstrate a typical approach that conforms to event oriented control models. However, acoustic sound is continuous, and highly dimensional, presenting a rich medium for computer interaction. Unsupervised machine learning models present great potential for real-time machine listening and understanding of audio and sound data. We propose a method for harnessing unsupervised machine learning algorithms, Adaptive Resonance Theory specifically, in order to inform machine listening, build musical context information, and drive real-time interactive performance systems. We present the design and evaluation of this model leveraging the expertise of trained, improvising musicians.\",\"PeriodicalId\":87287,\"journal\":{\"name\":\"IUI. International Conference on Intelligent User Interfaces\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-02-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IUI. International Conference on Intelligent User Interfaces\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2166966.2167021\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IUI. International Conference on Intelligent User Interfaces","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2166966.2167021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recent developments in machine listening present opportunities for innovative new paradigms for computer-human interaction. Voice recognition systems demonstrate a typical approach that conforms to event oriented control models. However, acoustic sound is continuous, and highly dimensional, presenting a rich medium for computer interaction. Unsupervised machine learning models present great potential for real-time machine listening and understanding of audio and sound data. We propose a method for harnessing unsupervised machine learning algorithms, Adaptive Resonance Theory specifically, in order to inform machine listening, build musical context information, and drive real-time interactive performance systems. We present the design and evaluation of this model leveraging the expertise of trained, improvising musicians.