A Dataset for Greek Traditional and Folk Music: Lyra

TL;DR

This paper introduces a comprehensive dataset of Greek Traditional and Folk music with rich metadata and clean recordings, along with a baseline deep learning model for classifying musical attributes, facilitating research in under-represented music traditions.

Contribution

The paper provides a new, detailed dataset for Greek traditional music, including metadata and high-quality recordings, and proposes a baseline deep learning approach for music classification tasks.

Findings

The dataset contains 1570 pieces totaling around 80 hours of audio.

A baseline deep learning model for music attribute recognition is established.

The dataset and models are publicly available for further research.

Abstract

Studying under-represented music traditions under the MIR scope is crucial, not only for developing novel analysis tools, but also for unveiling musical functions that might prove useful in studying world musics. This paper presents a dataset for Greek Traditional and Folk music that includes 1570 pieces, summing in around 80 hours of data. The dataset incorporates YouTube timestamped links for retrieving audio and video, along with rich metadata information with regards to instrumentation, geography and genre, among others. The content has been collected from a Greek documentary series that is available online, where academics present music traditions of Greece with live music and dance performance during the show, along with discussions about social, cultural and musicological aspects of the presented music. Therefore, this procedure has resulted in a significant wealth of descriptions regarding a variety of aspects, such as musical genre, places of origin and musical instruments. In addition, the audio recordings were performed under strict production-level specifications, in terms of recording equipment, leading to very clean and homogeneous audio content. In this work, apart from presenting the dataset in detail, we propose a baseline deep-learning classification approach to recognize the involved musicological attributes. The dataset, the baseline classification methods and the models are provided in public repositories. Future directions for further refining the dataset are also discussed.