A Dataset for Greek Traditional and Folk Music: Lyra

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

Characterizing and classifying music genres and subgenres via association analysis

In this thesis, we investigate the problem of automatic music genre classification in the field of Music Information Retrieval (MIR). MIR seeks to apply convenient automated solutions to many music-related tasks that are too tedious to perform by hand. These tasks often deal with vast quantities of music data. An effective automatic music genre classification approach may be useful for other tasks in MIR as well. Association analysis is a technique used to explore the inherent relationships among data objects in a problem domain. We present two novel approaches which capture genre characteristics through the use of association analysis on large music datasets. The first approach extracts the characteristic features of genres and uses these features to perform classification. The second approach attempts to improve on the first one by utilizing a pairwise dichotomy-like strategy. We then consider applying the second approach to the problem of automatic subgenre classification

The Greek Audio Dataset

Part 2: MHDW WorkshopInternational audienceThe Greek Audio Dataset (GAD), is a freely available collection of audio features and metadata for a thousand popular Greek tracks. In this work, the creation process of the dataset is described together with its contents. Following the methodology of existing datasets, the GAD dataset does not include the audio content of the respective data due to intellectual property rights but it includes MIR important features extracted directly from the content in addition to lyrics and manually annotated genre and mood for each audio track. Moreover, for each track a link to available audio content in YouTube is provided in order to support researchers that require the extraction of new feature-sets, not included in the GAD. The selection of the features extracted has been based on the Million Song Dataset in order to ensure that researchers do not require new programming interfaces in order to take advantage of the GAD