Ruben Hillewaere, Bernard Manderick and Darrell Conklin
Abstract: Music classification has been widely investigated in the past few years using a variety of machine learning approaches. In this study, a corpus of 3367 folk songs, divided into six geographic regions, has been created and is used to evaluate two popular yet contrasting methods for symbolic melody classification. For the task of folk song classification, a global feature approach, which summarizes a melody as a feature vector, is outperformed by an event model of abstract event features. The best accuracy obtained on the folk song corpus was achieved with an ensemble of event models. These results indicate that the event model should be the default model of choice for folk song classification.
Meinard Mueller, Peter Grosche and Frans Wiering
Abstract: Even though folk songs have been passed down mainly by oral tradition, most musicologists study the relation between folk songs on the basis of score-based transcriptions. Due to the complexity of audio recordings, once having the transcriptions, the original recorded tunes are often no longer studied in the actual folk song research though they still may contain valuable information. In this paper, we introduce an automated approach for segment- ing folk song recordings into its constituent stanzas, which can then be made accessible to folk song researchers by means of suitable visualization, searching, and navigation interfaces. Performed by elderly non-professional singers, the main challenge with the recordings is that most singers have serious problems with the intonation, fluctuating with their voices even over several semitones throughout a song. Using a combination of robust audio features along with various cleaning and audio matching strategies, our approach yields accurate segmentations even in the presence of strong deviations.
Notes: Interesting talk (as always) by Meinard about dealing with real world problems when dealing with folk song audio recordings.
Korinna Bade, Andreas Nurnberger, Sebastian Stober, Jörg Garbers and Frans Wiering
Abstract: In folk song research, appropriate similarity measures can be of great help, e.g. for classification of new tunes. Several measures have been developed so far. However, a particular musicological way of classifying songs is usually not directly reflected by just a single one of these measures. We show how a weighted linear combination of different basic similarity measures can be automatically adapted to a specific retrieval task by learning this metric based on a special type of constraints. Further, we describe how these constraints are derived from information provided by experts. In experiments on a folk song database, we show that the proposed approach outperforms the underlying basic similarity measures and study the effect of different levels of adaptation on the performance of the retrieval system.