Wang Zehui, Müller Matthias, Caffier Felix, Caffier Philipp P
Institute for Digital Transformation, University of Applied Sciences Ravensburg-Weingarten, Doggenriedstraße, 88250 Weingarten, Germany.
Occupational College of Music BFSM Krumbach, Mindelheimer Str. 47, 86381 Krumbach, Germany.
Diagnostics (Basel). 2023 Sep 6;13(18):2870. doi: 10.3390/diagnostics13182870.
Vocal arts medicine provides care and prevention strategies for professional voice disorders in performing artists. The issue of correct "Fach" determination depending on the presence of a lyric or dramatic voice structure is of crucial importance for opera singers, as chronic overuse often leads to vocal fold damage. To avoid phonomicrosurgery or prevent a premature career end, our aim is to offer singers an improved, objective fach counseling using digital sound analyses and machine learning procedures. For this purpose, a large database of 2004 sound samples from professional opera singers was compiled. Building on this dataset, we employed a classic ensemble learning method, namely the Random Forest algorithm, to construct an efficient fach classifier. This model was trained to learn from features embedded within the sound samples, subsequently enabling voice classification as either lyric or dramatic. As a result, the developed system can decide with an accuracy of about 80% in most examined voice types whether a sound sample has a lyric or dramatic character. To advance diagnostic tools and health in vocal arts medicine and singing voice pedagogy, further machine learning methods will be applied to find the best and most efficient classification method based on artificial intelligence approaches.
声乐艺术医学为表演艺术家的职业嗓音疾病提供护理和预防策略。对于歌剧演唱家而言，根据抒情或戏剧嗓音结构来正确确定“声部类型”这一问题至关重要，因为长期过度使用嗓音往往会导致声带损伤。为避免进行嗓音显微手术或防止职业生涯过早结束，我们的目标是利用数字声音分析和机器学习程序为歌手提供改进的、客观的声部类型咨询服务。为此，我们收集了一个包含2004个专业歌剧演唱家声音样本的大型数据库。基于这个数据集，我们采用了一种经典的集成学习方法，即随机森林算法，来构建一个高效的声部类型分类器。该模型经过训练，从声音样本中嵌入的特征进行学习，随后能够将嗓音分类为抒情或戏剧类型。结果，在大多数检测的嗓音类型中，所开发的系统能够以约80%的准确率判断一个声音样本具有抒情还是戏剧特征。为了推动声乐艺术医学和声乐教学中的诊断工具及健康发展，将应用更多机器学习方法，以基于人工智能方法找到最佳且最有效的分类方法。
