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Challenges of Music Recommendation Software

机译:音乐推荐软件的挑战

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摘要

In this paper, we present results of the analysis of a music identification application - Shazam Our aim was to discover if applications such as Shazam can improve organisational systems of performing rights organizations (PROs) in the area of precise monitoring of the songs used in the night club environment. The aim of the research was to evaluate how precise Shazam is in two different environments, the first one being a night club (where the music is loud and the signal to noise ratio is high) and the other one being home studio environment, where the signal to noise ratio is low. Each song was tagged for 10 seconds in two different environments and Shazam created two different audio fingerprints for each song based on some of the anchors of the simplified spectrogram and the target area between them. The conclusion is that Shazam application is not very accurate in the night club environment and, at this stage, cannot be used by performing rights organizations to monitor the music played in public spaces. In order for royalties to be collected and distributed correctly, one needs to have more precise instruments that offer the guarantee that music creators will get their due.
机译:在本文中,我们提出了音乐识别申请的分析结果 - Shazam我们的目的是发现Shazam等应用程序可以改善在精确监测中所用歌曲的精确监测领域的组织制度夜总会环境。该研究的目的是评估Shazam在两个不同的环境中的精确性,是夜总会的第一个(音乐很响亮,信噪比高),另一个是家庭工作室环境,在哪里信噪比为低。在两个不同的环境中,每首歌曲被标记为10秒钟,并且Shazam基于简化频谱图的一些锚点和它们之间的目标区域的锚点为每首歌创建了两首不同的音频指纹。结论是Shazam应用在夜总会环境中并不是很准确,在此阶段,不能通过执行权利组织来监控公共空间中播放的音乐。为了正确收集和分发的特许权使用费,有人需要拥有更精确的仪器,可以提供音乐创作者将获得其应付的保证。

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