云智能乐器交互

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
L. Turchet, J. Pauwels, C. Fischione, György Fazekas
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引用次数: 16

摘要

在知识共享许可下的大型在线音乐数据库很少由知名艺术家录制,因此传统的基于元数据的搜索不足以适应乐器演奏者的需求。新兴的智能乐器(SMIs)可以解决这一挑战。由于直接的internet连接和嵌入式处理,smi可以将请求发送到存储库,并为即兴创作、组合或学习目的再现响应。我们提出了一个智能吉他原型,它允许使用不同于传统音乐搜索的标准从大型在线音乐数据库中检索歌曲,这些标准来自对30名吉他手的采访。我们研究了三种与四种搜索标准(节奏、和弦、键和调音)相结合的交互方法,利用乐器的智能功能:(i)使用嵌入式触摸屏进行基于关键词的检索;(ii)云计算,将录制的内容传输到提取相关音频特征的服务器;(iii)边缘计算,其中吉他检测音频特征并直接发送请求。总的来说,初学者、中级演奏者和专家级演奏者对这些方法的评估表明,他们非常欣赏乐器与在线数据库的直接连接,以及基于实际音乐内容而不是传统文本标准(如歌曲名称或艺术家姓名)的搜索方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cloud-smart Musical Instrument Interactions
Large online music databases under Creative Commons licenses are rarely recorded by well-known artists, therefore conventional metadata-based search is insufficient in their adaptation to instrument players’ needs. The emerging class of smart musical instruments (SMIs) can address this challenge. Thanks to direct internet connectivity and embedded processing, SMIs can send requests to repositories and reproduce the response for improvisation, composition, or learning purposes. We present a smart guitar prototype that allows retrieving songs from large online music databases using criteria different from conventional music search, which were derived from interviewing 30 guitar players. We investigate three interaction methods coupled with four search criteria (tempo, chords, key and tuning) exploiting intelligent capabilities in the instrument: (i) keywords-based retrieval using an embedded touchscreen; (ii) cloud-computing where recorded content is transmitted to a server that extracts relevant audio features; (iii) edge-computing where the guitar detects audio features and sends the request directly. Overall, the evaluation of these methods with beginner, intermediate, and expert players showed a strong appreciation for the direct connectivity of the instrument with an online database and the approach to the search based on the actual musical content rather than conventional textual criteria, such as song title or artist name.
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来源期刊
CiteScore
5.20
自引率
3.70%
发文量
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