Towards efficient automated singer identification in large music databases

2006 ◽  
Author(s):  
Jialie Shen ◽  
Bin Cui ◽  
John Shepherd ◽  
Kian-Lee Tan
Keyword(s):  
Music Databases ◽  
Singer Identification

scholarly journals A novel framework for efficient automated singer identification in large music databases

2009 ◽  
Vol 27 (3) ◽  
pp. 1-31 ◽  
Author(s):  
Jialie Shen ◽  
John Shepherd ◽  
Bin Cui ◽  
Kian-Lee Tan
Keyword(s):  
Music Databases ◽  
Singer Identification

scholarly journals Genre of Bangla Music: A Machine Classification Learning Approach

2019 ◽  
Vol 18 (2) ◽  
pp. 66-72
Author(s):  
Abhijit Bhowmik ◽  
AZM Ehtesham Chowdhury
Keyword(s):  
Classification Systems ◽  
Learning Approach ◽  
Large Set ◽  
Automated Classification ◽  
Classification Learning ◽  
Genre Classification ◽  
Machine Learning Approach ◽  
Music Databases ◽  
East Asian Language

The necessity for designing autonomous indexing tools to establish expressive and efficient means of describing musical media content is well recognized. Music genre classification systems are significant to manage and use music databases. This research paper proposes an enhanced method to automatically classify music into different genre using a machine learning approach and presents the insight and results of the application of the proposed scheme to the classification of a large set of The Bangla music content, a South-East Asian language rich with a variety of music genres developed over many centuries. Building upon musical feature extraction and decision-making techniques, we propose new features and procedures to achieve enhanced accuracy. We demonstrate the efficacy of the proposed method by extracting features from a dataset of hundreds of The Bangla music pieces and testing the automatic classification decisions. This is the first development of an automated classification technique applied specifically to the Bangla music to the best of our knowledge, while the superior accuracy of the method makes it universally applicable.

Music Index with Full Text

2021 ◽  
Vol 23 (1) ◽  
pp. 35-37
Author(s):  
Alyson Vaaler
Keyword(s):  
Full Text ◽  
Music Databases ◽  
Sheer Size ◽  
Easy Integration ◽  
Comprehensive Coverage

Music Index with Full Text is an expansion of Music Index (formerly The Music Index Online), an EBSCO music periodical database that provides comprehensive coverage of the music field from 1970 to the present. Over 800 journals are indexed, and coverage includes various music styles and topics. Music Index with Full Text has added full text journal coverage from approximately 200 journals.The EBSCO interface is familiar to many users and offers easy integration with other heavily used music databases, such as RILM and RIPM. While the sheer size of citations and variety of music materials and styles is beneficial, Music Index might not be as useful to researchers focusing purely on historical music scholarship. The addition of full text journals is welcome, but the content of the journals varies widely in scope and content.

scholarly journals A deep learning method for Chinese singer identification

2019 ◽  
Vol 24 (4) ◽  
pp. 371-378 ◽  
Author(s):  
Zebang Shen ◽  
Binbin Yong ◽  
Gaofeng Zhang ◽  
Rui Zhou ◽  
Qingguo Zhou
Keyword(s):  
Deep Learning ◽  
Learning Method ◽  
Singer Identification

Mining in Music Databases

2011 ◽  
pp. 340-374
Author(s):  
Ioannis Karydis ◽  
Alexandros Nanopoulos ◽  
Yannis Manolopoulos
Keyword(s):  
Data Mining ◽  
Statistical Models ◽  
Complex Structure ◽  
Digital Audio ◽  
Practical Applications ◽  
Acoustic Data ◽  
Music Classification ◽  
Musical Scores ◽  
Music Databases ◽  
Music Data Mining

This chapter provides a broad survey of music data mining, including clustering, classification and pattern discovery in music. The data studied is mainly symbolic encodings of musical scores, although digital audio (acoustic data) is also addressed. Throughout the chapter, practical applications of music data mining are presented. Music data mining addresses the discovery of knowledge from music corpora. This chapter encapsulates the theory and methods required in order to discover knowledge in the form of patterns for music analysis and retrieval, or statistical models for music classification and generation. Music data, with their temporal, highly structured and polyphonic character, introduce new challenges for data mining. Additionally, due to their complex structure and their subjectivity to inaccuracies caused by perceptual effects, music data present challenges in knowledge representation as well.

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