The analysis of recorded audio material using computational methods has received increased attention in ethnomusicological research. We present a curated dataset of traditional Georgian vocal music for computational musicology. The corpus is based on historic tape recordings of three-voice Georgian songs performed by the the former master chanter Artem Erkomaishvili. In this article, we give a detailed overview of the audio material, transcriptions, and annotations contained in the dataset. Beyond its importance for ethnomusicological research, this carefully organized and annotated corpus constitutes a challenging scenario for music information retrieval tasks such as fundamental frequency estimation, onset detection, and score-to-audio alignment. The corpus is publicly available and accessible through score-following web-players.
Choral singing is a central part of musical cultures across the world, yet many facets of this widespread form of polyphonic singing are still to be explored. Music information retrieval (MIR) research on choral singing benefits from multitrack recordings of the individual singing voices. However, there exist only few publicly available multitrack datasets on polyphonic singing. In this paper, we present Dagstuhl ChoirSet (DCS), a multitrack dataset of a cappella choral music designed to support MIR research on choral singing. The dataset includes recordings of an amateur vocal ensemble performing two choir pieces in full choir and quartet settings. The audio data was recorded during an MIR seminar at Schloss Dagstuhl using different close-up microphones to capture the individual singers' voices. In this article, we give detailed insights into all stages of creating DCS: recording process, data preparation, generation of annotations as well as development of suitable interfaces for publicly accessing and reusing the data. Furthermore, we demonstrate the potential of the dataset for MIR research by discussing case studies on choral intonation assessment and multiple fundamental frequency (F0) estimation.
Due to the rapid developments in machine learning and the growing importance of opensource software, Python has become the predominant computer programming language for research and education in many scientific fields. While many engineering students on the Master's level have programming skills in different programming languages such as MATLAB, C/C++, or Java, they are often less experienced in using Python and the many associated software frameworks. The PCP notebooks contribute to closing this gap by offering open-source educational material for a Preparation Course for Python (PCP) while using signal processing as a motivating and tangible application for practicing the programming concepts. Building upon the open-access Jupyter notebook framework (Kluyver et al., 2016), the PCP notebooks consist of interactive documents that contain executable code, textbook-like explanations, mathematical formulas, plots, images, and sound examples. Assuming some general programming experience and basic knowledge in digital signal processing, the PCP notebooks are designed to serve several purposes. First of all, they introduce basic concepts of Python programming as required when participating in lab courses in a signal processing curriculum or when working with more advanced signalprocessing toolboxes. Furthermore, the notebooks recap central mathematical concepts needed in signal processing, including complex numbers, the exponential function, signals and sampling, and the discrete Fourier transform. Another goal of the course is to familiarize students with modern tools for software development and reproducible research. Providing interactive and well-structured material that may be used in a course or for self-study, we hope that the PCP notebooks make a valuable contribution in fostering education and research in multimedia engineering and beyond.
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