Automatic Tonic Identification in Indian Art Music: Approaches and Evaluation

The tonic is a fundamental concept in Indian art music. It is the base pitch, which an artist chooses in order to construct the melodies during a rg(a) rendition, and all accompanying instruments are tuned using the tonic pitch. Consequently, tonic identification is a fundamental task for most computational analyses of Indian art music, such as intonation analysis, melodic motif analysis and rg recognition. In this paper we review existing approaches for tonic identification in Indian art music and evaluate them on six diverse datasets for a thorough comparison and analysis. We study the performance of each method in different contexts such as the presence/absence of additional metadata, the quality of audio data, the duration of audio data, music tradition (Hindustani/Carnatic) and the gender of the singer (male/female). We show that the approaches that combine multi-pitch analysis with machine learning provide the best performance in most cases (90% identification accuracy on average), and are robust across the aforementioned contexts compared to the approaches based on expert knowledge. In addition, we also show that the performance of the latter can be improved when additional metadata is available to further constrain the problem. Finally, we present a detailed error analysis of each method, providing further insights into the advantages and limitations of the methods.

Modification of crosslinked chitosan membrane using NaY zeolite for pervaporation separation of water-isopropanol mixtures

The blocked diisocyanate crosslinked chitosan membrane was modified by incorporating different mass% of NaY zeolite. The physico-chemical properties of resulting composite membranes were studied using Fourier transform infrared spectroscopy (FTIR), wide-angle X-ray diffraction (WAXD), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) and scanning electron microscopy (SEM). The mechanical properties of the membranes were studied using universal testing machine (UTM). After measuring the equilibrium swelling, membranes were subjected to pervaporation for separation of water-isopropanol mixtures. Both flux and selectivity were increased with increasing NaY zeolite content in the membranes. The membrane containing 40 mass% of NaY zeolite exhibited the highest separation selectivity of 11,241 with a flux of 11.37 x 10(-2) kg/m(2) h for 10 mass% of water in the feed. The total flux and flux of water are almost overlapping each other, suggesting that these membranes could be effectively used to break the azeotropic point of water-isopropanol mixture. From the temperature dependent diffusion and permeation values, the Arrhenius activation parameters were estimated. All the composite membranes exhibited lower activation energy compared to crosslinked membrane, indicating that the permeants require less energy during the process because of molecular sieving action attributed to the presence of sodalite and super cages in the framework of Nay zeolite. The Henry's mode of sorption dominates the process, giving an endothermic contribution. (C) 2014 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Role of deformation temperature on the evolution and heterogeneity of texture during equal channel angular pressing of magnesium

Investigations on texture evolution and through-thickness texture heterogeneity during equal channel angular pressing (ECAP) of pure magnesium at 200 degrees C, 150 degrees C and room temperature (RT) was carried out by neutron, high energy synchrotron X-ray and electron back-scatter diffraction. Irrespective of the ECAP temperature, a distinctive basal (B) and pyramidal (C-2) II type of fibers forms. The texture differs in the bottom 1 mm portion, where the B-fiber is shifted similar to 55 degrees due to negative shear attributed to friction. (C) 2015 Elsevier Inc. All rights reserved.

MULTI-INSTRUMENT DETECTION IN POLYPHONIC MUSIC USING GAUSSIAN MIXTURE BASED FACTORIAL HMM

We formulate the problem of detecting the constituent instruments in a polyphonic music piece as a joint decoding problem. From monophonic data, parametric Gaussian Mixture Hidden Markov Models (GM-HMM) are obtained for each instrument. We propose a method to use the above models in a factorial framework, termed as Factorial GM-HMM (F-GM-HMM). The states are jointly inferred to explain the evolution of each instrument in the mixture observation sequence. The dependencies are decoupled using variational inference technique. We show that the joint time evolution of all instruments' states can be captured using F-GM-HMM. We compare performance of proposed method with that of Student's-t mixture model (tMM) and GM-HMM in an existing latent variable framework. Experiments on two to five polyphony with 8 instrument models trained on the RWC dataset, tested on RWC and TRIOS datasets show that F-GM-HMM gives an advantage over the other considered models in segments containing co-occurring instruments.

SPEECH ENHANCEMENT USING DISCRIMINATIVE RANDOM FIELDS

Speech enhancement in stationary noise is addressed using the ideal channel selection framework. In order to estimate the binary mask, we propose to classify each time-frequency (T-F) bin of the noisy signal as speech or noise using Discriminative Random Fields (DRF). The DRF function contains two terms - an enhancement function and a smoothing term. On each T-F bin, we propose to use an enhancement function based on likelihood ratio test for speech presence, while Ising model is used as smoothing function for spectro-temporal continuity in the estimated binary mask. The effect of the smoothing function over successive iterations is found to reduce musical noise as opposed to using only enhancement function. The binary mask is inferred from the noisy signal using Iterated Conditional Modes (ICM) algorithm. Sentences from NOIZEUS corpus are evaluated from 0 dB to 15 dB Signal to Noise Ratio (SNR) in 4 kinds of additive noise settings: additive white Gaussian noise, car noise, street noise and pink noise. The reconstructed speech using the proposed technique is evaluated in terms of average segmental SNR, Perceptual Evaluation of Speech Quality (PESQ) and Mean opinion Score (MOS).

Prussian blue as a single precursor for synthesis of Fe/Fe3C encapsulated N-doped graphitic nanostructures as bi-functional catalysts

We report a unique, single source precursor Prussian blue (iron(III) ferrocyanide (Fe-4(III)Fe-II(CN)(6)](3))) for the synthesis of Fe/Fe3C nanoparticle encapsulated N-doped graphitic layers and bamboo-like graphitic nanotubes. Hollow N-doped graphite (N-HG) nanostructures are obtained when the encapsulated nanostructures are treated with an acid. Both the encapsulated nanostructures and N-HG are shown to be applicable as bi-functional electrocatalysts for oxygen reduction (ORR) and oxygen evolution reactions (OER). The ORR activity is shown to be improved for N-HG and is comparable to commercial Pt/C. On the other hand, encapsulated nanostructures exhibit OER activity with long-term stability comparable to commercial RuO2.

Prussian blue as a single precursor for synthesis of Fe/Fe3C encapsulated N-doped graphitic nanostructures as bi-functional catalysts

We report a unique, single source precursor Prussian blue (iron(III) ferrocyanide (Fe-4(III)Fe-II(CN)(6)](3))) for the synthesis of Fe/Fe3C nanoparticle encapsulated N-doped graphitic layers and bamboo-like graphitic nanotubes. Hollow N-doped graphite (N-HG) nanostructures are obtained when the encapsulated nanostructures are treated with an acid. Both the encapsulated nanostructures and N-HG are shown to be applicable as bi-functional electrocatalysts for oxygen reduction (ORR) and oxygen evolution reactions (OER). The ORR activity is shown to be improved for N-HG and is comparable to commercial Pt/C. On the other hand, encapsulated nanostructures exhibit OER activity with long-term stability comparable to commercial RuO2.