Application of multiway methods for dimensinality reduction to music

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Journal Title
Journal ISSN
Volume Title
School of Science | Master's thesis
Date
2013
Major/Subject
Information and Computer Science
Mcode
T-110
Degree programme
Language
en
Pages
91
Series
Abstract
This thesis can be placed in the broader field of Music Information Retrieval (MIR). MIR refers to a huge set of strategies, software and tools through which computers can analyse and predict interesting patterns from audio data. It is a diverse and multidisciplinary field, encompassing fields like signal processing, machine learning, and musicology and music theory, to name a few. Methods of dimensionality reduction are widely used in data mining and machine learning. These help in reducing the complexity of the classification/clustering algorithms etc., used to process the data. They also help in studying some useful statistical properties of the dataset. In this Master's Thesis, a personalized music collection is taken and audio features are extracted from the songs, by using the Mel spectrogram. A music tensor is built from these features. Then, two approaches to unfold the tensor and convert it into a 2-way data matrix are studied. After unfolding the tensor, dimensionality reduction techniques like Principal Components Analysis (PCA) and classic metric Multidimensional Scaling (MDS) are applied. Unfolding the tensor and performing either MDS or PCA is equivalent to performing Multiway Principal Component Analysis (MPCA). A third method Multilevel Simultaneous Component Analysis (MLSCA), which builds a composite model for each song is also applied. The number of components to retain is obtained by hold-out validation. The fitness of each of these models were evaluated with the T2 and Q statistic, and compared with each other. The aim of this thesis is to produce a dimensionality reduction which can be used for further MIR tasks like better clustering of data with respect to e.g. artists / genres.
Description
Supervisor
Simula, Olli
Thesis advisor
Corona, Francesco
Miche, Yoan
Keywords
Mel spectrogram, MDS, MLSCA, MPCA, music collection, MIR, PCA
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