Matrix computations (3rd ed.)
What Size Test Set Gives Good Error Rate Estimates?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
A comparative study on content-based music genre classification
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Sparse Image Coding Using a 3D Non-Negative Tensor Factorization
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Non-negative tensor factorization with applications to statistics and computer vision
ICML '05 Proceedings of the 22nd international conference on Machine learning
Exploring Music Collections by Browsing Different Views
Computer Music Journal
Aggregate features and ADABOOST for music classification
Machine Learning
Music genre classification using MIDI and audio features
EURASIP Journal on Applied Signal Processing
Automatic genre classification of musical signals
EURASIP Journal on Applied Signal Processing
Novel Multi-layer Non-negative Tensor Factorization with Sparsity Constraints
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Tensor Decompositions and Applications
SIAM Review
Discriminant nonnegative tensor factorization algorithms
IEEE Transactions on Neural Networks
Controlling sparseness in non-negative tensor factorization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Musical Genre Classification Using Nonnegative Matrix Factorization-Based Features
IEEE Transactions on Audio, Speech, and Language Processing
Probabilistic and logic-based modelling of harmony
CMMR'10 Proceedings of the 7th international conference on Exploring music contents
International Journal of Speech Technology
Classification accuracy is not enough
Journal of Intelligent Information Systems
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Music genre classification techniques are typically applied to the data matrix whose columns are the feature vectors extracted from music recordings. In this paper, a feature vector is extracted using a texture window of one sec, which enables the representation of any 30 sec long music recording as a time sequence of feature vectors, thus yielding a feature matrix. Consequently, by stacking the feature matrices associated to any dataset recordings, a tensor is created, a fact which necessitates studying music genre classification using tensors. First, a novel algorithm for non-negative tensor factorization (NTF) is derived that extends the non-negative matrix factorization. Several variants of the NTF algorithm emerge by employing different cost functions from the class of Bregman divergences. Second, a novel supervised NTF classifier is proposed, which trains a basis for each class separately and employs basis orthogonalization. A variety of spectral, temporal, perceptual, energy, and pitch descriptors is extracted from 1000 recordings of the GTZAN dataset, which are distributed across 10 genre classes. The NTF classifier performance is compared against that of the multilayer perceptron and the support vector machines by applying a stratified 10-fold cross validation. A genre classification accuracy of 78.9% is reported for the NTF classifier demonstrating the superiority of the aforementioned multilinear classifier over several data matrix-based state-of-the-art classifiers.