12,256 research outputs found
Author Identification from Song Lyrics
No full textMachine Learning (ML) tools have been used extensively in a wide variety of domains
recently. Due the enormous amount of data being produced, machine learning techniques
are being heavily used to make sense of data & derive meaningful results. Using machine
learning tools, we can turn the data into knowledge.
Music is one of the truest forms of art. Bangladesh has a great history of music with a
great tradition of song writing over centuries. Authorship attribution is the way of
identifying the author from a linguistic corpus.
This paper demonstrates a guideline to identify the author of a Bengali song from the
lyrics of that song using machine learning. This research work presents the first work on
machine learning approach for author attribution from the lyrics of a song. Here six
methods of machine learning are used for the author identification and high accuracies
have been achieved from these methods. It is observed that Naïve Bayes method provides
higher accuracy in comparison with the other methods
Song
No full textAuthor attribution from Rudolph, 240. Printed on yellow paper with black ink. Set to the tune of "Happy land of Canaan". First line "You Rebels come along and listen to my song"
Orthogonal Subspace Projection Target Detector for Hyperspectral Anomaly Detection
No full textOrthogonal subspace projection (OSP) is a versatile hyperspectral imaging technique which has shown great potential in dimensionality reduction, target detection, spectral unmixing, etc. However, due to its inherent requirement of prior target knowledge, OSP has not been explored in anomaly detection. This article takes advantage of an unsupervised OSP-based algorithm, automatic target generation process (ATGP), and a recently developed OSP-go decomposition (OSP-GoDec) along with data sphering (DS) to make OSP applicable to anomaly detection. Its idea is to implement ATGP on the background (BKG) and target subspaces constructed from the low-rank matrix L and sparse matrix S generated by OSP-GoDec to derive an OSP-based anomaly detector (OSP-AD). In particular, OSP-AD also includes DS to remove BKG interference from the target subspace so as to enhance anomaly detection. Surprisingly, operating data samples on different constructions of the BKG subspace and the target subspace yields various versions of OSP-AD. Experiments show that given an appropriate construction of the BKG subspace and the target subspace, OSP-AD can be shown to outperform existing anomaly detectors including Reed-Xiaoli anomaly detector and collaborative representation-based anomaly detector (CRD).The work of Chein-I Chang was supported by the Fundamental Research Funds for Central Universities under Grant 3132019341. The work of Hongju Cao was supported by the Nature Science Foundation of Liaoning Province under Grant 20180550018. The work of Meiping Song was supported by the National Nature Science Foundation of China under Grant 61971082, Grant 61890964, and Grant 3132019341.https://ieeexplore.ieee.org/document/938709
The Singer or the Song? Developments in Performers' Rights from the Perspective of a Cultural Economist
No full textOver the last century, performers gradually acquired statutory protection of their economic and moral
rights. These rights are not copyright in the legal sense but neighboring rights and until recently, they
were mainly remuneration rights that are collectively administered. With the WPPT (WIPO
Performers and Phonograms Treaty), performers now have individual exclusive rights for digital
performances; this leads to the question: what has motivated this change – is it a change in the
perception of the value of performer or a change brought about by the changing technology of copying or,
indeed, a change that reflects different economic costs and benefits? The paper discusses the role of
copyright law as an incentive to performers and asks if the economic role of the performer is so different
from that of the author. The conclusion is that a complex interaction of the legal regulations, economic
conditions and institutional arrangements for administering these new rights will determine the outcome
Comment on “The relationship between Tang-Song poetry and zen buddhism thought”
No full textCommented article: TIAN, T. The relationship between Tang-Song poetry and Zen Buddhism thought. Trans/Form/Ação: Unesp journal of philosophy, Marília, v. 47, n. 4, “Eastern thought”, e0240064, 2024. Available at: https://revistas.marilia.unesp.br/index.php/transformacao/article/view/14581
Freemasons\u27 Song
No full textSong concerning pride in Freemasonryhttps://egrove.olemiss.edu/kgbsides_uk/1560/thumbnail.jp
Northumberland Election Song
No full textA song for a political candidate.https://egrove.olemiss.edu/kgbsides_uk/1899/thumbnail.jp
Orthogonal Subspace Projection-Based Go-Decomposition Approach to Finding Low-Rank and Sparsity Matrices for Hyperspectral Anomaly Detection
No full textLow-rank and sparsity-matrix decomposition (LRaSMD) has received considerable interests lately. One of effective methods for LRaSMD is called go decomposition (GoDec), which finds low-rank and sparse matrices iteratively subject to the predetermined low-rank matrix order m and sparsity cardinality k. This article presents an orthogonal subspace-projection (OSP) version of GoDec to be called OSPGoDec, which implements GoDec in an iterative process by a sequence of OSPs to find desired low-rank and sparse matrices. In order to resolve the issues of empirically determining p = m + j and k, the well-known virtual dimensionality (VD) is used to estimate p in conjunction with the Kuybeda et al. developed minimax-singular value decomposition (MX-SVD) in the maximum orthogonal complement algorithm (MOCA) to estimate k. Consequently, LRaSMD can be realized by implementing OSP-GoDec using p and k determined by VD and MX-SVD, respectively. Its application to anomaly detection demonstrates that the proposed OSP-GoDec coupled with VD and MX-SVD performs very effectively and better than the commonly used LRaSMD-based anomaly detectors.The work of Chein-I Chang was supported by the Fundamental Research Funds for Central Universities under Grant 3132019341. The work of Hongju Cao was supported by the Nature Science Foundation of Liaoning Province under Grant 20180550018. The work of Meiping Song was supported by the National Nature Science Foundation of China under Grant 61601077, Grant 61971082, and Grant 61890964.https://ieeexplore.ieee.org/document/914035
Song of Haymakers
No full textA song about working in the hayfields during summer.https://egrove.olemiss.edu/kgbsides_uk/1628/thumbnail.jp
Appendix to Esther's Song
No full textNotes - 'Esther's Song' with descriptive entries on friends, associates, and family members (125 pages)Appendi
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