14 resultados para outdoor music
em Indian Institute of Science - Bangalore - Índia
Resumo:
Multistress aging of outdoor composite polymeric insulators continues to be a topic of interest for power transmission research community. Aging due to dry conditions alone at elevated temperatures and electric stress in the presence of UV radiation environment probably has not been explored. This paper deals with long-term accelerated multistress aging under the above conditions on full-scale 11 kV distribution class composite silicone rubber insulators. To evaluate the long-term synergistic effect of electric stress, temperature and UV radiation on insulators, they were subjected to accelerated aging in a specially designed multistress-aging chamber for 12000 hours. Chemical, physical and electrical changes due to degradation have been assessed using various techniques. It has been found that the content of low molecular weight molecules and hydrophobicity reduced significantly. Also, due to oxidation and aging there is appreciable increase in surface roughness and weight percentage of oxygen. Study is under progress and only intermediate results are presented in this paper.
Resumo:
Polymeric outdoor insulators are being increasingly used for electrical power transmission and distribution in the recent years. One of the current topics of interest for the power transmission community is the aging of such outdoor polymeric insulators. A few research groups are carrying out aging studies at room temperature with wet period as an integral part of multistress aging cycle as specified by IEC standards. However, aging effect due to dry conditions alone at elevated temperatures and electric stress in the presence of radiation environment has probably not been explored. It is interesting to study and understand the insulator performance under dry conditions where wet periods are either rare or absent and to estimate the extent of aging caused by multiple stresses. This paper deals with the long-term accelerated multistress aging on full-scale 11 kV distribution class composite silicone rubber insulators. In order to assess the long-term synergistic effect of electric stress, temperature and UV radiation on insulators, they are subjected to accelerated aging in a specially designed multistress-aging chamber for 3800 hours. All the stresses are applied at an accelerated level. Using a data acquisition system developed for the work, leakage current has been monitored in LabVIEW environment. Chemical changes due to degradations have been studied using Energy Dispersive X-Ray analysis, Scanning Electron Microscope and Fourier transform Infrared Spectroscopy. Periodically different parameters like low molecular weight (LMW) molecular content, hydrophobicity, leakage current and surface morphology were monitored. The aging study is under progress and only intermediate results are presented in this paper.
Resumo:
The problem of automatic melody line identification in a MIDI file plays an important role towards taking QBH systems to the next level. We present here, a novel algorithm to identify the melody line in a polyphonic MIDI file. A note pruning and track/channel ranking method is used to identify the melody line. We use results from musicology to derive certain simple heuristics for the note pruning stage. This helps in the robustness of the algorithm, by way of discarding "spurious" notes. A ranking based on the melodic information in each track/channel enables us to choose the melody line accurately. Our algorithm makes no assumption about MIDI performer specific parameters, is simple and achieves an accuracy of 97% in identifying the melody line correctly. This algorithm is currently being used by us in a QBH system built in our lab.
Resumo:
We propose a simple speech music discriminator that uses features based on HILN(Harmonics, Individual Lines and Noise) model. We have been able to test the strength of the feature set on a standard database of 66 files and get an accuracy of around 97%. We also have tested on sung queries and polyphonic music and have got very good results. The current algorithm is being used to discriminate between sung queries and played (using an instrument like flute) queries for a Query by Humming(QBH) system currently under development in the lab.
Resumo:
In the direction of arrival (DOA) estimation problem, we encounter both finite data and insufficient knowledge of array characterization. It is therefore important to study how subspace-based methods perform in such conditions. We analyze the finite data performance of the multiple signal classification (MUSIC) and minimum norm (min. norm) methods in the presence of sensor gain and phase errors, and derive expressions for the mean square error (MSE) in the DOA estimates. These expressions are first derived assuming an arbitrary array and then simplified for the special case of an uniform linear array with isotropic sensors. When they are further simplified for the case of finite data only and sensor errors only, they reduce to the recent results given in [9-12]. Computer simulations are used to verify the closeness between the predicted and simulated values of the MSE.
Resumo:
Multistress aging/weathering of outdoor composite polymeric insulators has been a topic of interest for power transmission research community in the last few decades. This paper deals with the long-term accelerated weathering of full-scale distribution class silicone rubber composite insulators. To evaluate the long-term synergistic effect of electric stress, temperature and UV radiation on insulators, they were subjected to accelerated weathering in a specially designed multistress-aging chamber for 30,000 h. All the insulators were subjected to the same level of electrical and thermal stresses but different UV radiation levels. Chemical, physical and electrical changes due to degradation have been assessed using various techniques. It was found that there was a monotonous reduction of the content of low molecular weight (LMW) molecules with the duration of the weathering. Further, due to oxidation and weathering there is an appreciable increase in surface roughness and atomic percentage of oxygen. There is no change in the leakage current of new and aged insulators under both wet and dry conditions at the end of the aging. The results also indicate that there is no influence of UV radiation on the silicone rubber for the durations and conditions under which the studies were made.
Resumo:
Microalgae are the most sought after sources for biofuel production due to their capacity to utilize carbon and synthesize it into high density liquid. Current energy crisis have put microalgae under scanner for economical production of biodiesel. Modifications like physiological stress and genetic variation is done to increase the lipid yield of the microalgae. A study was conducted using a microalgal consortium for a period of 15 days to evaluate the feasibility of algal biomass from laboratory as well as outdoor culture conditions. Native algal strains were isolated from a tropical freshwater lake. Preliminary growth studies indicated the relationship between the nitrates and phosphates to the community structure through the days. The lipid profile done using Gas chromatography – Mass spectrometry, revealed the profile of the algal community. Resource competition led to isolation of algae, aided in the lipid profile of a single alga. However, further studies on the application of the mixed population are required to make this consortium approach economically viable for producing algae biofuels.
Resumo:
We analyze the AlApana of a Carnatic music piece without the prior knowledge of the singer or the rAga. AlApana is ameans to communicate to the audience, the flavor or the bhAva of the rAga through the permitted notes and its phrases. The input to our analysis is a recording of the vocal AlApana along with the accompanying instrument. The AdhAra shadja(base note) of the singer for that AlApana is estimated through a stochastic model of note frequencies. Based on the shadja, we identify the notes (swaras) used in the AlApana using a semi-continuous GMM. Using the probabilities of each note interval, we recognize swaras of the AlApana. For sampurNa rAgas, we can identify the possible rAga, based on the swaras. We have been able to achieve correct shadja identification, which is crucial to all further steps, in 88.8% of 55 AlApanas. Among them (48 AlApanas of 7 rAgas), we get 91.5% correct swara identification and 62.13% correct R (rAga) accuracy.
Resumo:
Compressive Sensing (CS) is a new sensing paradigm which permits sampling of a signal at its intrinsic information rate which could be much lower than Nyquist rate, while guaranteeing good quality reconstruction for signals sparse in a linear transform domain. We explore the application of CS formulation to music signals. Since music signals comprise of both tonal and transient nature, we examine several transforms such as discrete cosine transform (DCT), discrete wavelet transform (DWT), Fourier basis and also non-orthogonal warped transforms to explore the effectiveness of CS theory and the reconstruction algorithms. We show that for a given sparsity level, DCT, overcomplete, and warped Fourier dictionaries result in better reconstruction, and warped Fourier dictionary gives perceptually better reconstruction. “MUSHRA” test results show that a moderate quality reconstruction is possible with about half the Nyquist sampling.
Resumo:
Music signals comprise of atomic notes drawn from a musical scale. The creation of musical sequences often involves splicing the notes in a constrained way resulting in aesthetically appealing patterns. We develop an approach for music signal representation based on symbolic dynamics by translating the lexicographic rules over a musical scale to constraints on a Markov chain. This source representation is useful for machine based music synthesis, in a way, similar to a musician producing original music. In order to mathematically quantify user listening experience, we study the correlation between the max-entropic rate of a musical scale and the subjective aesthetic component. We present our analysis with examples from the south Indian classical music system.
Resumo:
We address the problem of multi-instrument recognition in polyphonic music signals. Individual instruments are modeled within a stochastic framework using Student's-t Mixture Models (tMMs). We impose a mixture of these instrument models on the polyphonic signal model. No a priori knowledge is assumed about the number of instruments in the polyphony. The mixture weights are estimated in a latent variable framework from the polyphonic data using an Expectation Maximization (EM) algorithm, derived for the proposed approach. The weights are shown to indicate instrument activity. The output of the algorithm is an Instrument Activity Graph (IAG), using which, it is possible to find out the instruments that are active at a given time. An average F-ratio of 0 : 7 5 is obtained for polyphonies containing 2-5 instruments, on a experimental test set of 8 instruments: clarinet, flute, guitar, harp, mandolin, piano, trombone and violin.
Resumo:
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.
Resumo:
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.