Extracting semantic information from an on-line Carnatic music forum
Publication Type:
Conference PaperSource:
Int. Soc. for Music Information Retrieval Conf. (ISMIR), Porto, Portugal, p.355-360 (2012)URL:
http://ismir2012.ismir.net/event/papers/355-ismir-2012.pdfAbstract:
By mining user-generated text content we can obtain music-related information that could not otherwise be extracted from audio signals or symbolic score representations. In this paper we propose a methodology for extracting musically-relevant semantic information from an online discussion forum, rasikas.org, dedicated to the Carnatic music tradition. For that we define a dictionary of relevant terms such as raagas, taalas, performers, composers, and instruments, and create a complex network representation by matching such dictionary against the forum posts. This network representation is used to identify popular terms within the forum, as well as relevant co-occurrences and semantic relationships. This way, for instance, we are able to guess the instrument of a performer with 95% accuracy, to discover the confusion between two raagas with different naming conventions, or to infer semantic relationships regarding lineage or musical influence. This contribution is a first step towards the creation of ontologies for a culture-specific art music tradition.
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Quantifying the evolution of popular music
Publication Type:
Conference PaperSource:
No Lineal, Zaragoza, Spain (2012)Abstract:
Popular music is a key cultural expression that has captured listeners' attention for ages. Many of the structural regularities underlying musical discourse are yet to be discovered and, accordingly, their historical evolution remain formally unknown. In this contribution we use tools and concepts from statistical physics and complex networks to study and quantify the evolution of western contemporary popular music. In it, we unveil a number of patterns and metrics characterizing the generic usage of primary musical facets such as pitch, timbre, and loudness. Moreover, we find many of these patterns and metrics to be consistently stable for a period of more than fifty years, thus pointing towards a great degree of conventionalism in this type of music. Nonetheless, we prove important changes or trends. These are related to the restriction of pitch transitions, the homogenization of the timbral palette, and the growing loudness levels. The obtained results suggest that our perception of new popular music would be largely rooted on these changing characteristics. Hence, an old tune could perfectly sound novel and fashionable, provided that it consisted of common harmonic progressions, changed the instrumentation, and increased the average loudness.
Patterns, regularities, and evolution of contemporary popular music
Publication Type:
Conference PaperSource:
Complexitat.Cat, Barcelona (2012)URL:
http://www.complexitat.cat/seminars/112/Abstract:
Popular music is a key cultural expression that has captured listeners' attention for ages. Many of the structural regularities underlying musical discourse are yet to be discovered and, accordingly, their historical evolution remain formally unknown. In this contribution we use tools and concepts from statistical physics and complex networks to study and quantify the evolution of western contemporary popular music. In it, we unveil a number of patterns and metrics characterizing the generic usage of primary musical facets such as pitch, timbre, and loudness. Moreover, we find many of these patterns and metrics to be consistently stable for a period of more than fifty years, thus pointing towards a great degree of conventionalism in this type of music. Nonetheless, we prove important changes or trends. These are related to the restriction of pitch transitions, the homogenization of the timbral palette, and the growing loudness levels. The obtained results suggest that our perception of new popular music would be largely rooted on these changing characteristics. Hence, an old tune could perfectly sound novel and fashionable, provided that it consisted of common harmonic progressions, changed the instrumentation, and increased the average loudness.
Combining two lazy learning methods for classification and knowledge discovery.
Publication Type:
Conference PaperSource:
International Conference on Knowledge Discovery and Information Retrieval, INSTICC, Senart, Paris (2011)Keywords:
Machine Learning; Lazy learning methods; knowledge discovery; classification; medical diagnosisAbstract:
The goal of this paper is to construct a classifier for diagnosing malignant melanoma. We experimented with two lazy learning methods, $k$-NN and \textsf{LID}, and compared their results with the ones produced by decision trees. We performed this comparison because we are also interested on building a domain model that can serve as basis to dermatologists to propose a good characterization of early melanomas. We shown that lazy learning methods have a better performance than decision trees in terms of sensitivity and specificity. We have seen that both lazy learning methods produce complementary results ($k$-NN has high specificity and LID has high sensitivity) suggesting that a combination of both could be a good classifier. We report experiments confirming this point. Concerning the construction of a domain model, we propose to use the explanations provided by the lazy learning methods, and we see that the resulting theory is as predictive and useful as the one obtained from decision trees.
Classification of Melanomas in situ using Knowledge Discovery with Explained CBR
Publication Type:
Journal ArticleSource:
Artificial Intelligence in Medicine, Volume 51, p.12 (2011)Keywords:
knowledge discovery; classification; clustering; CBR; applicationAbstract:
The goal of Knowledge Discovery is to extract knowledge from a set of data. Most common techniques used in knowledge discovery are based on clustering methods whose goal is to analyze a set of objects and to obtain clusters based on the similarity between these objects. A desirable characteristic of clustering results is that they should be easily understandable by domain experts. In this paper we introduce LazyCL, a procedure using a lazy learning method to produce explanations on clusters of unlabeled cases. These explanations are the basis on which experts can perform knowledge discovery. Here we use LazyCL to generate a domain theory for classification of melanomas in situ.
