Audio classification for music information retrieval of Hindustani vocal music

An important task in music information retrieval of Indian art music is the recognition of the larger musicological frameworks, called ragas, on which the performances are based. Ragas are characterized by prominent musical notes, motifs, general sequences of notes used and embellishments improvised by the performers. In this work we propose a convolutional neural network-based model to work on the mel-spectrograms for classication of steady note regions and note transition regions in vocal melodies which can be used for finding prominent musical notes. It is demonstrated that, good classification accuracy is obtained using the proposed model.

Polynomial and power series ring extensions from sequences

Let [Formula: see text] be a commutative ring with identity. Let [Formula: see text] and [Formula: see text] be the collection of polynomials and, respectively, of power series with coefficients in [Formula: see text]. There are a lot of multiplications in [Formula: see text] and [Formula: see text] such that together with the usual addition, [Formula: see text] and [Formula: see text] become rings that contain [Formula: see text] as a subring. These multiplications are from a class of sequences [Formula: see text] of positive integers. The trivial case of [Formula: see text], i.e. [Formula: see text] for all [Formula: see text], gives the usual polynomial and power series ring. The case [Formula: see text] for all [Formula: see text] gives the well-known Hurwitz polynomial and Hurwitz power series ring. In this paper, we study divisibility properties of these polynomial and power series ring extensions for general sequences [Formula: see text] including UFDs and GCD-domains. We characterize when these polynomial and power series ring extensions are isomorphic to each other. The relation between them and the usual polynomial and power series ring is also presented.

Small Informational Size and Interim Cores of Large Quasilinear Economies

This paper provides nonemptiness results of approximate interim cores with endogenous communication in large quasilinear economies, where every agent’s informational size is small. We offer results for both replica and more general sequences of economies.

THE NON-COMMUTATIVE KHINTCHINE INEQUALITIES FOR

We give a proof of the Khintchine inequalities in non-commutative $L_{p}$-spaces for all $0<p<1$. These new inequalities are valid for the Rademacher functions or Gaussian random variables, but also for more general sequences, for example, for the analogues of such random variables in free probability. We also prove a factorization for operators from a Hilbert space to a non-commutative $L_{p}$-space, which is new for $0<p<1$. We end by showing that Mazur maps are Hölder on semifinite von Neumann algebras.

Frequent Closed Partial Orders Mining in Sequences

Mining partial orders from sequence data is an important data mining task with broad applications. As partial orders mining is a NP-hard problem, many efficient pruning algorithm have been proposed. In this paper, we improve a classical algorithm of discovering frequent closed partial orders from string. For general sequences, we consider items appearing together having equal chance to calculate the detecting matrix used for pruning. Experimental evaluations from a real data set show that our algorithm can effectively mine FCPO from sequences.

Super-Resolution Reconstruction of Video Sequences by GST-Driven Adaptive Filtering

A GST-driven spatially adaptive filter is developed in this paper based on the framework of non-local means (NLM) avoiding explicit motion estimation. Gradient Structure Tensor (GST) is introduced to express the underlying local image structural patterns, which drives the window function to yield adaptive scale and shape fitting for the local structure. This leads to patches with more similar gray-level and local structures being gathered for super-resolution estimation of image. Results on several test video sequences show that the proposed method is effective in providing super-resolution on general sequences and achieves improvement of performance on the compared method.