  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.
