19,419 research outputs found
Antipattern discovery in Basque folk tunes
This paper presents a new pattern discovery method for labelled folk song corpora. The method discovers
general patterns that are rare or even entirely absent in a corpus, and among those the ones that are the most general
or frequent in the background set. The method is applied to two parallel ontologies of a large corpus of Basque folk
tunes
Mining Characteristic Patterns for Comparative Music Corpus Analysis
A core issue of computational pattern mining is the identification of interesting patterns. When mining music corpora organized into classes of songs, patterns may be of interest because they are characteristic, describing prevalent properties of classes, or because they are discriminant, capturing distinctive properties of classes. Existing work in computational music corpus analysis has focused on discovering discriminant patterns. This paper studies characteristic patterns, investigating the behavior of different pattern interestingness measures in balancing coverage and discriminability of classes in top k pattern mining and in individual top ranked patterns. Characteristic pattern mining is applied to the collection of Native American music by Frances Densmore, and the discovered patterns are shown to be supported by Densmore’s own analyses
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Big Chord Data Extraction and Mining
Harmonic progression is one of the cornerstones of tonal music composition and is thereby essential to many musical styles and traditions. Previous studies have shown that musical genres and composers could be discriminated based on chord progressions modeled as chord n-grams. These studies were however conducted on small-scale datasets and using symbolic music transcriptions.
In this work, we apply pattern mining techniques to over 200,000 chord progression sequences out of 1,000,000 extracted from the I Like Music (ILM) commercial music audio collection. The ILM collection spans 37 musical genres and includes pieces released between 1907 and 2013. We developed a single program multiple data parallel computing approach whereby audio feature extraction tasks are split up and run simultaneously on multiple cores. An audio-based chord recognition model (Vamp plugin Chordino) was used to extract the chord progressions from the ILM set. To keep low-weight feature sets, the chord data were stored using a compact binary format. We used the CM-SPADE algorithm, which performs a vertical mining of sequential patterns using co-occurence information, and which is fast and efficient enough to be applied to big data collections like the ILM set. In orderto derive key-independent frequent patterns, the transition between chords are modeled by changes of qualities (e.g. major, minor, etc.) and root keys (e.g. fourth, fifth, etc.). The resulting key-independent chord progression patterns vary in length (from 2 to 16) and frequency (from 2 to 19,820) across genres. As illustrated by graphs generated to represent frequent 4-chord progressions, some patterns like circle-of-fifths movements are well represented in most genres but in varying degrees.
These large-scale results offer the opportunity to uncover similarities and discrepancies between sets of musical pieces and therefore to build classifiers for search and recommendation. They also support the empirical testing of music theory. It is however more difficult to derive new hypotheses from such dataset due to its size. This can be addressed by using pattern detection algorithms or suitable visualisation which we present in a companion study
Chicago Music City
Chicago Music City compares the strength and vitality of music industries and scenes across the United States. Sociologists, urban planners, and real-estate developers point to quality of life and availability of cultural amenities as important indicators of the health and future success of urban areas. Economic impact studies show the importance of music to local economies. This publication compares Chicago's musical strength with the 50 largest metropolitan areas in the U.S., focusing on 11 comparison cities: Chicago and its demographic peers, New York and Los Angeles, and eight other cities with strong musical reputations -- Atlanta, Austin, Boston, Las Vegas, Memphis, Nashville, New Orleans and Seattle
Data Mining in Electronic Commerce
Modern business is rushing toward e-commerce. If the transition is done
properly, it enables better management, new services, lower transaction costs
and better customer relations. Success depends on skilled information
technologists, among whom are statisticians. This paper focuses on some of the
contributions that statisticians are making to help change the business world,
especially through the development and application of data mining methods. This
is a very large area, and the topics we cover are chosen to avoid overlap with
other papers in this special issue, as well as to respect the limitations of
our expertise. Inevitably, electronic commerce has raised and is raising fresh
research problems in a very wide range of statistical areas, and we try to
emphasize those challenges.Comment: Published at http://dx.doi.org/10.1214/088342306000000204 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Comparing New World Traditions: Appalachian Balladry And The Mexican Corrido
This work compares and Appalachian balladry and the Mexican corrido in several ways. First, how both traditions developed in the New World from a shared European ballad tradition, how both regions have been described as cultural borderlands, and the historic and rapidly increasing presence of Mexicans in Appalachia. Second, how their lyrics of femicide seemingly reinforce patriarchal values but can be used by singers to discuss cultural values. Third, how the two traditions have been shaped by conflict to produce oppositional themes and forms; border corridos being shaped by conflict between ethnic groups, and protest songs by Kentucky ballad singers being shaped by class conflict. These conflicts of gender, class, and ethnicity are more often than not inter-related. Finally, how these similarities and continuing in-migration might suggest the incorporation of the corrido into the region’s musical practices. Through examination of ballad text, summary of ballad scholarship, and interviews with North Carolina ballad singers Sheila Kay Adams and Rick Ward I argue that beyond the symbolic uses related to conflict and oppression proclaimed by scholars, ballad singing provides a safe and sometimes discrete way for singers to discuss and interpret cultural values or express personal emotions in ways that words cannot
Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations
This paper explores a new natural language processing task, review-driven
multi-label music style classification. This task requires the system to
identify multiple styles of music based on its reviews on websites. The biggest
challenge lies in the complicated relations of music styles. It has brought
failure to many multi-label classification methods. To tackle this problem, we
propose a novel deep learning approach to automatically learn and exploit style
correlations. The proposed method consists of two parts: a label-graph based
neural network, and a soft training mechanism with correlation-based continuous
label representation. Experimental results show that our approach achieves
large improvements over the baselines on the proposed dataset. Especially, the
micro F1 is improved from 53.9 to 64.5, and the one-error is reduced from 30.5
to 22.6. Furthermore, the visualized analysis shows that our approach performs
well in capturing style correlations
Using fMRI in experimental philosophy: Exploring the prospects
This chapter analyses the prospects of using neuroimaging methods, in particular functional magnetic resonance imaging (fMRI), for philosophical purposes. To do so, it will use two case studies from the field of emotion research: Greene et al. (2001) used fMRI to uncover the mental processes underlying moral intuitions, while Lindquist et al. (2012) used fMRI to inform the debate around the nature of a specific mental process, namely, emotion. These studies illustrate two main approaches in cognitive neuroscience: Reverse inference and ontology testing, respectively. With regards to Greene et al.’s study, the use of Neurosynth (Yarkoni 2011) will show that the available formulations of reverse inference, although viable a priori, seem to be of limited use in practice. On the other hand, the discussion of Lindquist et al.’s study will present the so far neglected potential of ontology-testing approaches to inform philosophical questions
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