AMS and genre classification using timbre, rhythm and tempo models

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Towards efficient music genre classification using FastMap

Automatic genre classification aims to correctly categorize an unknown recording with a music genre. Recent studies use the Kullback-Leibler (KL) divergence to estimate music similarity then perform classification using k-nearest neighbours (k-NN). However, this approach is not practical for large databases. We propose an efficient genre classifier that addresses the scalability problem. It uses a combination of modified FastMap algorithm and KL divergence to return the nearest neighbours then use 1- NN for classification. Our experiments showed that high accuracies are obtained while performing classification in less than 1/20 second per track

Enhancing timbre model using MFCC and its time derivatives for music similarity estimation

One of the popular methods for content-based music similarity estimation is to model timbre with MFCC as a single multivariate Gaussian with full covariance matrix, then use symmetric Kullback-Leibler divergence. From the field of speech recognition, we propose to use the same approach on the MFCCs’ time derivatives to enhance the timbre model. The Gaussian models for the delta and acceleration coefficients are used to create their respective distance matrix. The distance matrices are then combined linearly to form a full distance matrix for music similarity estimation. In our experiments on two datasets, our novel approach performs better than using MFCC alone.Moreover, performing genre classification using k-NN showed that the accuracies obtained are already close to the state-of-the-art

Using a Factored Dual in Augmented Lagrangian Methods for Semidefinite Programming

In the context of augmented Lagrangian approaches for solving semidefinite programming problems, we investigate the possibility of eliminating the positive semidefinite constraint on the dual matrix by employing a factorization. Hints on how to deal with the resulting unconstrained maximization of the augmented Lagrangian are given. We further use the approximate maximum of the augmented Lagrangian with the aim of improving the convergence rate of alternating direction augmented Lagrangian frameworks. Numerical results are reported, showing the benefits of the approach.Comment: 7 page

IIB Supergravity and the E6(6) covariant vector-tensor hierarchy

IIB supergravity is reformulated with a manifest local USp(8) invariance that makes the embedding of five-dimensional maximal supergravities transparent. In this formulation the ten-dimensional theory exhibits all the 27 one-form fields and 22 of the 27 two-form fields that are required by the vector-tensor hierarchy of the five-dimensional theory. The missing 5 two-form fields must transform in the same representation as a descendant of the ten-dimensional `dual graviton'. The invariant E6(6) symmetric tensor that appears in the vector-tensor hierarchy is reproduced. Generalized vielbeine are derived from the supersymmetry transformations of the vector fields, as well as consistent expressions for the USp(8) covariant fermion fields. Implications are discussed for the consistency of the truncation of IIB supergravity compactified on the five-sphere to maximal gauged supergravity in five space-time dimensions with an SO(6) gauge group.Comment: 48 pages, added an extra affiliatio