Від шляхетства до дворянства: до історії польської присутності на Ніжинщині в ХVІІ – першій третині ХІХ століття (за матеріалами формулярних списків)

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Placing Music Artists and Songs in Time Using Editorial Metadata and Web Mining Techniques

This paper investigates the novel task of situating music artists and songs in time, thereby adding contextual information that typically correlates with an artist’s similarities, collaborations and influences. The proposed method makes use of editorial metadata in conjunction with web mining techniques, aiming to infer an artist’s productivity over time and estimate the original year of release of a song. Experimental evaluation over a set of Dutch and American music confirms the practicality and reliability of the proposed methods. As a consequence, large-scale correlational analyses between artist productivity and other musical characteristics (e.g. versatility, eminence) become possible

Placing Music Artists and Songs in Time Using Editorial Metadata and Web Mining Techniques

This paper investigates the novel task of situating music artists and songs in time, thereby adding contextual information that typically correlates with an artist’s similarities, collaborations and influences. The proposed method makes use of editorial metadata in conjunction with web mining techniques, aiming to infer an artist’s productivity over time and estimate the original year of release of a song. Experimental evaluation over a set of Dutch and American music confirms the practicality and reliability of the proposed methods. As a consequence, large-scale correlational analyses between artist productivity and other musical characteristics (e.g. versatility, eminence) become possible

Automatic Segmentation and Deep Learning of Bird Sounds

We present a study on automatic birdsong recognition with deep neural networks using the BIRDCLEF2014 dataset. Through deep learning, feature hierarchies are learned that represent the data on several levels of abstraction. Deep learning has been applied with success to problems in fields such as music information retrieval and image recognition, but its use in bioacoustics is rare. Therefore, we investigate the application of a common deep learning technique (deep neural networks) in a classification task using songs from Amazonian birds. We show that various deep neural networks are capable of outperforming other classification methods. Furthermore, we present an automatic segmentation algorithm that is capable of separating bird sounds from non-bird sounds

Cognition-inspired Descriptors for Scalable Cover Song Retrieval

Inspired by representations used in music cognition studies and computational musicology, we propose three simple and interpretable descriptors for use in mid- to high-level computational analysis of musical audio and applications in content-based retrieval. We also argue that the task of scalable cover song retrieval is very suitable for the de- velopment of descriptors that effectively capture musical structures at the song level. The performance of the proposed descriptions in a cover song problem is presented. We further demonstrate that, due to the musically-informed nature of the descriptors, an independently established model of stability and variation in covers songs can be integrated to improve performance

Automatic Segmentation and Deep Learning of Bird Sounds

We present a study on automatic birdsong recognition with deep neural networks using the BIRDCLEF2014 dataset. Through deep learning, feature hierarchies are learned that represent the data on several levels of abstraction. Deep learning has been applied with success to problems in fields such as music information retrieval and image recognition, but its use in bioacoustics is rare. Therefore, we investigate the application of a common deep learning technique (deep neural networks) in a classification task using songs from Amazonian birds. We show that various deep neural networks are capable of outperforming other classification methods. Furthermore, we present an automatic segmentation algorithm that is capable of separating bird sounds from non-bird sounds