30,557 research outputs found
Building a document genre corpus: a profile of the KRYS I corpus
This paper describes the KRYS I corpus, consisting of documents classified into 70 genre classes. It has
been constructed as part of an effort to automate document genre classification as distinct from topic
detection. Previously there has been very little work on building corpora of texts which have been classified
using a nontopical
genre palette. The reason for this is partly due to the fact that genre as a concept, is
rooted in philosophy, rhetoric and literature, and highly complex and domain dependent in its interpretation
([11]). The usefulness of genre in everyday information search is only now starting to be recognised and
there is no genre classification schema that has been consolidated to have applicable value in this direction.
By presenting here our experiences in constructing the KRYS I corpus, we hope to shed light on the
information gathering and seeking behaviour and the role of genre in these activities, as well as a way
forward for creating a better corpus for testing automated genre classification tasks and the application of
these tasks to other domains.
Feature Type Analysis in Automated Genre Classification
In this paper, we compare classifiers based on language model, image, and stylistic features for automated genre classification. The majority of previous studies in genre classification have created models based on an amalgamated representation of a document using a multitude of features. In these models, the inseparable roles of different features make it difficult to determine a means of improving the classifier when it exhibits poor performance in detecting selected genres. By independently modeling and comparing classifiers based on features belonging to three types, describing visual, stylistic, and topical properties, we demonstrate that different genres have distinctive feature strengths.
Building a Document Genre Corpus: a Profile of the KRYS I Corpus
This paper describes the KRYS I corpus (http://www.krys-corpus.eu/Info.html), consisting of documents classified into 70 genre classes. It has been constructed as part of an effort to automate document genre classification as distinct from topic detection. Previously there has been very little work on building corpora of texts which have been classified using a non-topical genre palette. The reason for this is partly due to the fact that genre as a concept, is rooted in philosophy, rhetoric and literature, and highly complex and domain dependent in its interpretation ([11]). The usefulness of genre in everyday information search is only now starting to be recognised and there is no genre classification schema that has been consolidated to have applicable value in this direction. By presenting here our experiences in constructing the KRYS I corpus, we hope to shed light on the information gathering and seeking behaviour and the role of genre in these activities, as well as a way forward for creating a better corpus for testing automated genre classification tasks and the application of these tasks to other domains
Optical Music Recognition with Convolutional Sequence-to-Sequence Models
Optical Music Recognition (OMR) is an important technology within Music
Information Retrieval. Deep learning models show promising results on OMR
tasks, but symbol-level annotated data sets of sufficient size to train such
models are not available and difficult to develop. We present a deep learning
architecture called a Convolutional Sequence-to-Sequence model to both move
towards an end-to-end trainable OMR pipeline, and apply a learning process that
trains on full sentences of sheet music instead of individually labeled
symbols. The model is trained and evaluated on a human generated data set, with
various image augmentations based on real-world scenarios. This data set is the
first publicly available set in OMR research with sufficient size to train and
evaluate deep learning models. With the introduced augmentations a pitch
recognition accuracy of 81% and a duration accuracy of 94% is achieved,
resulting in a note level accuracy of 80%. Finally, the model is compared to
commercially available methods, showing a large improvements over these
applications.Comment: ISMIR 201
Instrumentation
published or submitted for publicatio
Multimodal music information processing and retrieval: survey and future challenges
Towards improving the performance in various music information processing
tasks, recent studies exploit different modalities able to capture diverse
aspects of music. Such modalities include audio recordings, symbolic music
scores, mid-level representations, motion, and gestural data, video recordings,
editorial or cultural tags, lyrics and album cover arts. This paper critically
reviews the various approaches adopted in Music Information Processing and
Retrieval and highlights how multimodal algorithms can help Music Computing
applications. First, we categorize the related literature based on the
application they address. Subsequently, we analyze existing information fusion
approaches, and we conclude with the set of challenges that Music Information
Retrieval and Sound and Music Computing research communities should focus in
the next years
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