You Call That Singing? Ensemble Classification for Multi-Cultural Collections of Music Recordings

The wide range of vocal styles, musical textures and recording techniques found in ethnomusicological field recordings leads us to consider the problem of automatically labeling the content to know whether a recording is a song or instrumental work. Furthermore, if it is a song, we are interested in labeling aspects of the vocal texture: e.g. solo, choral, acapella or singing with instruments. We present evidence to suggest that automatic annotation is feasible for recorded collections exhibiting a wide range of recording techniques and representing musical cultures from around the world. Our experiments used the Alan Lomax Cantometrics training tapes data set, to encourage future comparative evaluations. Experiments were conducted with a labeled subset consisting of several hundred tracks, annotated at the track and frame levels, as acapella singing, singing plus instruments or instruments only. We trained frame-by-frame SVM classifiers using MFCC features on positive and negative exemplars for two tasks: per-frame labeling of singing and acapella singing. In a further experiment, the frame-by-frame classifier outputs were integrated to estimate the predominant content of whole tracks. Our results show that frame-byframe classifiers achieved 71% frame accuracy and whole track classifier integration achieved 88% accuracy. We conclude with an analysis of classifier errors suggesting avenues for developing more robust features and classifier strategies for large ethnographically diverse collections.