Noise measurements of a phase fluorometric instrument

We present noise measurements of a phase fluorometric oxygen sensor that sets the limits of accuracy for this instrument. We analyze the phase sensitive detection measurement system with the signal ''shot'' noise being the only significant contribution to the system noise. Based on the modulated optical power received by the photomultiplier, the analysis predicts a noise spectral power density that was within 3 dB of the measured power spectral noise density. Our results demonstrate that at a received optical power of 20 fW the noise level was low enough to permit the detection of a change oxygen concentration of 1% at the sensor. We also present noise measurements of a new low-cost version of this instrument that uses a photodiode instead of a photomultiplier. These measurements show that the noise for this instrument was limited by noise generated in the preamplifier following the photodiode. (C) 1996 Society of Photo-Optical Instrumentation Engineers.

A high precision instrument to measure angular and binocular deviation introduced by aircraft windscreens by using a shadow casting technique

Objects viewed through transparent sheets with residual non-parallelism and irregularity appear shifted and distorted. This distortion is measured in terms of angular and binocular deviation of an object viewed through the transparent sheet. The angular and binocular deviations introduced are particularly important in the context of aircraft windscreens and canopies as they can interfere with decision making of pilots especially while landing, leading to accidents. In this work, we have developed an instrument to measure both the angular and binocular deviations introduced by transparent sheets. This instrument is especially useful in the qualification of aircraft windscreens and canopies. It measures the deviation in the geometrical shadow cast by a periodic dot pattern trans-illuminated by the distorted light beam from the transparent test specimen compared to the reference pattern. Accurate quantification of the shift in the pattern is obtained by cross-correlating the reference shadow pattern with the specimen shadow pattern and measuring the location of the correlation peak. The developed instrument is handy to use and computes both angular and binocular deviation with an accuracy of less than +/- 0.1 mrad (approximate to 0.036 mrad) and has an excellent repeatability with an error of less than 2%. (C) 2012 American Institute of Physics. http://dx.doi.org/10.1063/1.4769756]

STUDENT'S-t MIXTURE MODEL BASED MULTI-INSTRUMENT RECOGNITION IN POLYPHONIC MUSIC

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.

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.