Learning Distributed Representations for Multiple-Viewpoint Melodic Prediction

The analysis of sequences is important for extracting in- formation from music owing to its fundamentally temporal nature. In this paper, we present a distributed model based on the Restricted Boltzmann Machine (RBM) for learning melodic sequences. The model is similar to a previous suc- cessful neural network model for natural language [2]. It is first trained to predict the next pitch in a given pitch se- quence, and then extended to also make use of information in sequences of note-durations in monophonic melodies on the same task. In doing so, we also propose an efficient way of representing this additional information that takes advantage of the RBM’s structure. Results show that this RBM-based prediction model performs better than previ- ously evaluated n-gram models and also outperforms them in certain cases. It is able to make use of information present in longer sequences more effectively than n-gram models, while scaling linearly in the number of free pa- rameters required

The Recurrent Temporal Discriminative Restricted Boltzmann Machines

Classification of sequence data is the topic of interest for dynamic Bayesian models and Recurrent Neural Networks (RNNs). While the former can explicitly model the temporal dependencies between class variables, the latter have a capability of learning representations. Several attempts have been made to improve performance by combining these two approaches or increasing the processing capability of the hidden units in RNNs. This often results in complex models with a large number of learning parameters. In this paper, a compact model is proposed which offers both representation learning and temporal inference of class variables by rolling Restricted Boltzmann Machines (RBMs) and class variables over time. We address the key issue of intractability in this variant of RBMs by optimising a conditional distribution, instead of a joint distribution. Experiments reported in the paper on melody modelling and optical character recognition show that the proposed model can outperform the state-of-the-art. Also, the experimental results on optical character recognition, part-of-speech tagging and text chunking demonstrate that our model is comparable to recurrent neural networks with complex memory gates while requiring far fewer parameters

Automatic Phrase Continuation from Guitar and Bass guitar Melodies

A framework is proposed for generating interesting, musically similar variations of a given monophonic melody. The focus is on pop/rock guitar and bass guitar melodies with the aim of eventual extensions to other instruments and musical styles. It is demonstrated here how learning musical style from segmented audio data can be formulated as an unsupervised learning problem to generate a symbolic representation. A melody is first segmented into a sequence of notes using onset detection and pitch estimation. A set of hierarchical, coarse-to-fine symbolic representations of the melody is generated by clustering pitch values at multiple similarity thresholds. The variance ratio criterion is then used to select the appropriate clustering levels in the hierarchy. Note onsets are aligned with beats, considering the estimated meter of the melody, to create a sequence of symbols that represent the rhythm in terms of onsets/rests and the metrical locations of their occurrence. A joint representation based on the cross-product of the pitch cluster indices and metrical locations is used to train the prediction model, a variable-length Markov chain. The melodies generated by the model were evaluated through a questionnaire by a group of experts, and received an overall positive response. </jats:p

A Distributed Model For Multiple-Viewpoint Melodic Prediction.

The analysis of sequences is important for extracting information from music owing to its fundamentally temporal nature. In this paper, we present a distributed model based on the Restricted Boltzmann Machine (RBM) for melodic sequences. The model is similar to a previous successful neural network model for natural language [2]. It is first trained to predict the next pitch in a given pitch sequence, and then extended to also make use of information in sequences of note-durations in monophonic melodies on the same task. In doing so, we also propose an efficient way of representing this additional information that takes advantage of the RBM’s structure. In our evaluation, this RBM-based prediction model performs slightly better than previously evaluated n-gram models in most cases. Results on a corpus of chorale and folk melodies showed that it is able to make use of information present in longer contexts more effectively than n-gram models, while scaling linearly in the number of free parameters required

The Netflix plus model: Can subscription financing improve access to medicines in low- And middle-income countries?

At present, pay for prescription models are insufficient at containing costs and improving access to medicines. Subscription financing through tenders, licensing fees and unrestricted or fixed volumes can benefit stakeholders across the supply chain. Pharmaceutical manufacturers can reduce the need for marketing expenses and gain certainty in revenue. This will decrease costs, improve predictability in budget expenditure for payers and remove price as a barrier of access from patients. Inherently, low- and middle-income countries lack the purchasing power to leverage price discounts through typical price arrangements. These markets can realise substantial savings for branded and generic medicines through subscription financing. Procuring of on-patent and off-patent drugs requires separate analysis for competition effects, the length of contract and encouraging innovation in the medicine pipeline. Prices of competitive on-patent medicines and orphan drugs can be reduced through increased competition and volume. Furthermore, pooling expertise and resources through joint procurement has the potential for greater savings. Incentivising research and development within the pharmaceutical industry is essential for sustaining a competitive market, preventing monopolies and improving access to expensive treatments. However, technical capacity, forecasting demand and the quality of generic medicines present limitations which necessitate government support and international partnerships. Ultimately, improving access requires progressive financing mechanisms with patients and cost containment in mind

Generalising the Discriminative Restricted Boltzmann Machine

We present a novel theoretical result that generalises the Discriminative Restricted Boltzmann Machine (DRBM). While originally the DRBM was defined assuming the {0, 1}-Bernoulli distribution in each of its hidden units, this result makes it possible to derive cost functions for variants of the DRBM that utilise other distributions, including some that are often encountered in the literature. This is illustrated with the Binomial and {-1, +1}-Bernoulli distributions here. We evaluate these two DRBM variants and compare them with the original one on three benchmark datasets, namely the MNIST and USPS digit classification datasets, and the 20 Newsgroups document classification dataset. Results show that each of the three compared models outperforms the remaining two in one of the three datasets, thus indicating that the proposed theoretical generalisation of the DRBM may be valuable in practice

Design, testing and optimization of a microfluidic device for capture and concentration of bacteria

Effective detection of bacterial pathogens in large sample volumes is a challenging problem. Pre-concentration routines currently in practice before the actual detection process are cumbersome and hard to automate. An effort is made to address the problem of volume discrepancy between day-to-day samples and the concentrated samples needed for analysis. Principles of conceptual design are used in formulating the âÂÂNeed StatementâÂÂ, âÂÂFunction Structureâ and in identifying the âÂÂCritical Design Parametersâ and âÂÂDesign ConstraintsâÂÂ. Electrokinetic phenomena are used to exploit the surface charges on bacteria. Electrophoresis is used to transport the bacteria to electrode surface and âÂÂElectrostatic trappingâ is then used to capture these microbes on the electrode surface. The captured microbes can then be concentrated in a concentrator unit. A prototype microfluidic device is fabricated for showing the proof of concept. Optimization is done to minimize hydraulic power consumption and wetted volume. Observations from the initial prototype device along with the optimization results are used in building a new prototype device. Operation of this device is demonstrated by capture of bacteria from flow. Qualitative studies are conducted and preliminary quantification is also done