Journal ArticleDOI
Beyond Timbral Statistics: Improving Music Classification Using Percussive Patterns and Bass Lines
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TLDR
Experimental results show that the automatically calculated rhythmic pattern information and bass pattern information can be used to effectively classify musical genre/style and improve upon current approaches based on timbral features.Abstract:
This paper discusses a new approach for clustering sequences of bar-long percussive and bass-line patterns in audio music collections and its application to genre classification. Many musical genres and styles are characterized by two kinds of distinct representative patterns, i.e., percussive patterns and bass-line patterns. So far, in most automatic genre classification systems, rhythmic and bass melody information has not been effectively used. In order to extract bar-long unit rhythmic patterns for a music collection, we propose a clustering method based on one-pass dynamic programming and k-means clustering. For clustering bass-line patterns, a method based on k -means clustering capable of handling pitch-shifting is proposed. After extracting these two fundamental kinds of patterns for each style/genre, feature vectors which are suitable for representing information about the patterns are proposed for supervised learning. Experimental results show that the automatically calculated rhythmic pattern information and bass pattern information can be used to effectively classify musical genre/style and improve upon current approaches based on timbral features.read more
Citations
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A Survey of Music Recommendation Systems and Future Perspectives
TL;DR: A motivation-based model for music recommender is proposed using the empirical studies of human behaviour, sports education, music psychology, and music psychology to propose a general framework and state-of-art approaches in recommending music.
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The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use
TL;DR: This article disprove the claims that all MGR systems are affected in the same ways by these faults, and that the performances of M GR systems in GTZAN are still meaningfully comparable since they all face the same faults.
Book ChapterDOI
A survey of evaluation in music genre recognition
TL;DR: This paper compiles a bibliography of work in MGR, and analyzes three aspects of evaluation: experimental designs, datasets, and figures of merit.
Proceedings Article
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TL;DR: It is found that spectral features outperform those based on rhythm, dynamics, and, to a lesser extent, harmony, and that the fusion of different feature sets does not always lead to improved classification.
Proceedings ArticleDOI
The need for music information retrieval with user-centered and multimodal strategies
TL;DR: It is argued that Music-IR approaches with multimodal and user-centered strategies are necessary to serve real-life usage patterns and maintain and improve accessibility of digital music data.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Book
Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
Journal ArticleDOI
Indexing by Latent Semantic Analysis
TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.
Proceedings Article
Algorithms for Non-negative Matrix Factorization
Daniel D. Lee,H. Sebastian Seung +1 more
TL;DR: Two different multiplicative algorithms for non-negative matrix factorization are analyzed and one algorithm can be shown to minimize the conventional least squares error while the other minimizes the generalized Kullback-Leibler divergence.
Journal ArticleDOI
Musical genre classification of audio signals
George Tzanetakis,Perry R. Cook +1 more
TL;DR: The automatic classification of audio signals into an hierarchy of musical genres is explored and three feature sets for representing timbral texture, rhythmic content and pitch content are proposed.