作者简介：
Iben Have, Aarhus University
Kenneth Enevoldsen, Aarhus University
转载来源：Digital Humanities Quarterly, 2021, Volume 15 Number1，http://www.digitalhumanities.org/dhq/vol/15/1/000522/000522.html
数字化改变了流音乐广播。来自Spotify和iTunes等音乐流媒体服务的竞争在很大程度上超过了传统的播放列表电台，上世纪90年代，公共服务电台通过软件生成的播放列表在全球范围内的传播，已经取代了热情的音乐电台主持人。但数字化也改变了我们研究无线电的方式。在丹麦，从1989年开始，几乎所有的广播节目都进行了数字化，这使得人们能够真正聆听这些档案，来研究广播内容的历史变化。本文通过定性案例研究与新的大规模研究相比较，探讨了丹麦广播公司(Danish Broadcasting Corporation) P3广播频道1989-2019年音乐与谈话的分布情况。从方法论上讲，这种从近距离收听少数节目到远距离收听超过65,000小时的广播的转变，使我们能够对两种分析的方法、结果、优缺点进行讨论和批判性比较。先前的研究已经证明，用于音频频谱图像识别的卷积神经网络(CNNs)优于其他方法，如支持向量机(svm)。提出的大规模研究表明，基于cnn的方法可以很好地概括丹麦广播的语音和音乐分类，即使没有微调，总体准确率达到98%。
作者简介：
Iben Have
Iben Have is associate professor in Media Studies, School of Communication and Culture, Aarhus University, Denmark. She holds a PhD from Musicology and has specialized in research of sound and media in different constellations across the fields of reception-, text-, and institutional analysis. She is co-founder and editor in chief of the open access journal SoundEffects and has been managing the digital radio archive and infrastructure LARM.fm.
Kenneth Enevoldsen
PhD student at the Center for Humanities Computing Aarhus, at Aarhus University. His current research is on multimodal representation learning with a primary focus on how to meaningfully combine data sources from disparate domains into meaningful representations. He is especially interested in applied machine learning in the humanities and arts.