Towards Automated Processing of Folk Song Recordings

Folk music is closely related to the musical culture of a specific nation or region. Even though folk songs have been passed down mainly by oral tradition, most musicologists study the relation between folk songs on the basis of symbolic music descriptions, which are obtained by transcribing recorded tunes into a score-like representation. Due to the complexity of audio recordings, once having the transcriptions, the original recorded tunes are often no longer used in the actual folk song research even though they still may contain valuable information. In this paper, we present various techniques for making audio recordings more easily accessible for music researchers. In particular, we show how one can use synchronization techniques to automatically segment and annotate the recorded songs. The processed audio recordings can then be made accessible along with a symbolic transcript by means of suitable visualization, searching, and navigation interfaces to assist folk song researchers to conduct large scale investigations comprising the audio material

Segmentation of Folk Songs with a Probabilistic Model

Structure is an important aspect of music. Musical structure can be recognized in different musical modalities such as rhythm, melody, harmony or lyrics and plays a crucial role in our appreciation of music. In recent years many researchers have addressed the problem of music segmentation, mainly for popular and classical music. Some of the more recent approaches are Mauch et al. (2009), Foote (2000), Serr`a et al. (2012) and McFee & Ellis (2014). Last three are included in the music structure analysis framework MSAF Nieto & Bello (2015). None of the mentioned approaches however, addresses the specifics of folk music. While commercial music is performed by professional performers and recorded with professional equipment in suitable recording conditions, this is usually not true for folk music field recordings, which are recorded in everyday environments and contain music performed by amateur performers. Thus, recordings may contain high levels of background noise, equipment induced noise (e.g. hum) and reverb, as well as performer mistakes such as inaccurate pitches, false starts, forgotten melody/lyrics or pitch drift throughout the performance. One of the most recent approaches which addressed folk music specifics was presented by M¨uller et al. (2013). The approach was designed for solo singing and was evaluated on a collection of Dutch folk music by Muller et al. (2010). In our paper, we present a novel folk music segmentation method, which also addresses folk music specifics and is designed to work well with a variety of ensemble types (solo, choir, instrumental and mixtures)

A computational study on outliers in world music

The comparative analysis of world music cultures has been the focus of several ethnomusicological studies in the last century. With the advances of Music Information Retrieval and the increased accessibility of sound archives, large-scale analysis of world music with computational tools is today feasible. We investigate music similarity in a corpus of 8200 recordings of folk and traditional music from 137 countries around the world. In particular, we aim to identify music recordings that are most distinct compared to the rest of our corpus. We refer to these recordings as ‘outliers’. We use signal processing tools to extract music information from audio recordings, data mining to quantify similarity and detect outliers, and spatial statistics to account for geographical correlation. Our findings suggest that Botswana is the country with the most distinct recordings in the corpus and China is the country with the most distinct recordings when considering spatial correlation. Our analysis includes a comparison of musical attributes and styles that contribute to the ‘uniqueness’ of the music of each country

Probabilistic Segmentation of Folk Music Recordings

The paper presents a novel method for automatic segmentation of folk music field recordings. The method is based on a distance measure that uses dynamic time warping to cope with tempo variations and a dynamic programming approach to handle pitch drifting for finding similarities and estimating the length of repeating segment. A probabilistic framework based on HMM is used to find segment boundaries, searching for optimal match between the expected segment length, between-segment similarities, and likely locations of segment beginnings. Evaluation of several current state-of-the-art approaches for segmentation of commercial music is presented and their weaknesses when dealing with folk music are exposed, such as intolerance to pitch drift and variable tempo. The proposed method is evaluated and its performance analyzed on a collection of 206 folk songs of different ensemble types: solo, two- and three-voiced, choir, instrumental, and instrumental with singing. It outperforms current commercial music segmentation methods for noninstrumental music and is on a par with the best for instrumental recordings. The method is also comparable to a more specialized method for segmentation of solo singing folk music recordings

Proceedings of the 6th International Workshop on Folk Music Analysis, 15-17 June, 2016

The Folk Music Analysis Workshop brings together computational music analysis and ethnomusicology. Both symbolic and audio representations of music are considered, with a broad range of scientific approaches being applied (signal processing, graph theory, deep learning). The workshop features a range of interesting talks from international researchers in areas such as Indian classical music, Iranian singing, Ottoman-Turkish Makam music scores, Flamenco singing, Irish traditional music, Georgian traditional music and Dutch folk songs. Invited guest speakers were Anja Volk, Utrecht University and Peter Browne, Technological University Dublin