4-01 - Classifying Leitmotifs in Recordings of Operas by Richard Wagner
Michael Krause, Frank Zalkow, Julia Zalkow, Christof Weiss, Meinard Müller
Keywords: MIR tasks, Automatic classification, Applications, Music retrieval systems, Domain knowledge, Machine learning/Artificial intelligence for music, Evaluation, datasets, and reproducibility, Novel datasets and use cases, Pattern matching and detection, Musical features and properties, Melody and motives
Abstract: From the 19th century on, several composers of Western opera made use of leitmotifs (short musical ideas referring to semantic entities such as characters, places, items, or feelings) for guiding the audience through the plot and illustrating the events on stage. A prime example of this compositional technique is Richard Wagner’s four-opera cycle Der Ring des Nibelungen. Across its different occurrences in the score, a leitmotif may undergo considerable musical variations. The concrete leitmotif instances in an audio recording are subject to acoustic variability. Our paper approaches the task of classifying such leitmotif instances in audio recordings. As our main contribution, we conduct a case study on a dataset covering 16 recorded performances of the Ring with annotations of ten central leitmotifs, leading to 2403 occurrences and 38448 instances in total. We build a neural network classification model and evaluate its ability to generalize across different performances and leitmotif occurrences. Our findings demonstrate the possibilities and limitations of leitmotif classification in audio recordings and pave the way towards the fully automated detection of leitmotifs in music recordings.