Novel melody line identification algorithm for polyphonic MIDI music

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.

A speech-music discriminator using HILN model based features

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.

A microcomputer program to analyze the CD spectrum of proteins and nucleic acids—Use of LOTUS 1-2-3 spread sheet

A user friendly interactive computer program, CIRDIC, is developed which calculates the molar ellipticity and molar circular dichroic absorption coefficients from the CD spectrum. This, in combination with LOTUS 1-2-3 spread sheet, will give the spectra of above parameters vs wavelength. The code is implemented in MicroSoft FORTRAN 77 which runs on any IBM compatible PC under MSDOS environment.

Development of a structured program for conversion to prenex normal form

The method of structured programming or program development using a top-down, stepwise refinement technique provides a systematic approach for the development of programs of considerable complexity. The aim of this paper is to present the philosophy of structured programming through a case study of a nonnumeric programming task. The problem of converting a well-formed formula in first-order logic into prenex normal form is considered. The program has been coded in the programming language PASCAL and implemented on a DEC-10 system. The program has about 500 lines of code and comprises 11 procedures.

HELANAL: A program to characterise helix geometry in proteins,

A detailed analysis of structural and position dependent characteristic features of helices will give a better understanding of the secondary structure formation in globular proteins. Here we describe an algorithm that quantifies the geometry of helices in proteins on the basis of their C-alpha atoms alone. The Fortran program HELANAL can extract the helices from the PDB files and then characterises the overall geometry of each helix as being linear, curved or kinked, in terms of its local structural features, viz. local helical twist and rise, virtual torsion angle, local helix origins and bending angles between successive local helix axes. Even helices with large radius of curvature are unambiguously identified as being linear or curved. The program can also be used to differentiate a kinked helix and other motifs, such as helix-loop-helix or a helix-turn-helix (with a single residue linker) with the help of local bending angles. In addition to these, the program can also be used to characterise the helix start and end as well as other types of secondary structures.

Indian Nanoelectronics Users Program An Outreach Vehicle to Expedite Nanoelectronics Research in India

The worldwide research in nanoelectronics is motivated by the fact that scaling of MOSFETs by conventional top down approach will not continue for ever due to fundamental limits imposed by physics even if it is delayed for some more years. The research community in this domain has largely become multidisciplinary trying to discover novel transistor structures built with novel materials so that semiconductor industry can continue to follow its projected roadmap. However, setting up and running a nanoelectronics facility for research is hugely expensive. Therefore it is a common model to setup a central networked facility that can be shared with large number of users across the research community. The Centres for Excellence in Nanoelectronics (CEN) at Indian Institute of Science, Bangalore (IISc) and Indian Institute of Technology, Bombay (IITB) are such central networked facilities setup with funding of about USD 20 million from the Department of Information Technology (DIT), Ministry of Communications and Information Technology (MCIT), Government of India, in 2005. Indian Nanoelectronics Users Program (INUP) is a missionary program not only to spread awareness and provide training in nanoelectronics but also to provide easy access to the latest facilities at CEN in IISc and at IITB for the wider nanoelectronics research community in India. This program, also funded by MCIT, aims to train researchers by conducting workshops, hands-on training programs, and providing access to CEN facilities. This is a unique program aiming to expedite nanoelectronics research in the country, as the funding for projects required for projects proposed by researchers from around India has prior financial approval from the government and requires only technical approval by the IISc/ IITB team. This paper discusses the objectives of INUP, gives brief descriptions of CEN facilities, the training programs conducted by INUP and list various research activities currently under way in the program.

Finite data performance of MUSIC and minimum norm methods

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.

Compiler-Directed Dynamic Voltage Scaling using Program Phases

Energy consumption has become a major constraint in providing increased functionality for devices with small form factors. Dynamic voltage and frequency scaling has been identified as an effective approach for reducing the energy consumption of embedded systems. Earlier works on dynamic voltage scaling focused mainly on performing voltage scaling when the CPU is waiting for memory subsystem or concentrated chiefly on loop nests and/or subroutine calls having sufficient number of dynamic instructions. This paper concentrates on coarser program regions and for the first time uses program phase behavior for performing dynamic voltage scaling. Program phases are annotated at compile time with mode switch instructions. Further, we relate the Dynamic Voltage Scaling Problem to the Multiple Choice Knapsack Problem, and use well known heuristics to solve it efficiently. Also, we develop a simple integer linear program formulation for this problem. Experimental evaluation on a set of media applications reveal that our heuristic method obtains a 38% reduction in energy consumption on an average, with a performance degradation of 1% and upto 45% reduction in energy with a performance degradation of 5%. Further, the energy consumed by the heuristic solution is within 1% of the optimal solution obtained from the ILP approach.

Carnatic music analysis: Shadja, swara identification and rAga verification in AlApana using stochastic models

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.

SuSeFLAV: A program for calculating supersymmetric spectra and lepton flavour violation

The program SuSeFLAV is introduced for computing supersymmetric mass spectra with flavour violation in various supersymmetric breaking scenarios with/without see-saw mechanism. A short user guide summarizing the compilation, executables and the input files is provided.

Implications of program phase behavior on timing analysis

Knowledge about program worst case execution time (WCET) is essential in validating real-time systems and helps in effective scheduling. One popular approach used in industry is to measure execution time of program components on the target architecture and combine them using static analysis of the program. Measurements need to be taken in the least intrusive way in order to avoid affecting accuracy of estimated WCET. Several programs exhibit phase behavior, wherein program dynamic execution is observed to be composed of phases. Each phase being distinct from the other, exhibits homogeneous behavior with respect to cycles per instruction (CPI), data cache misses etc. In this paper, we show that phase behavior has important implications on timing analysis. We make use of the homogeneity of a phase to reduce instrumentation overhead at the same time ensuring that accuracy of WCET is not largely affected. We propose a model for estimating WCET using static worst case instruction counts of individual phases and a function of measured average CPI. We describe a WCET analyzer built on this model which targets two different architectures. The WCET analyzer is observed to give safe estimates for most benchmarks considered in this paper. The tightness of the WCET estimates are observed to be improved for most benchmarks compared to Chronos, a well known static WCET analyzer.

Compressive sensing for music signals: comparison of transforms with coherent dictionaries

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.