Adaptive Hidden Markov Noise Modelling for Speech Enhancement

A robust and reliable noise estimation algorithm is required in many speech enhancement systems. The aim of this thesis is to propose and evaluate a robust noise estimation algorithm for highly non-stationary noisy environments. In this work, we model the non-stationary noise using a set of discrete states with each state representing a distinct noise power spectrum. In this approach, the state sequence over time is conveniently represented by a Hidden Markov Model (HMM). In this thesis, we first present an online HMM re-estimation framework that models time-varying noise using a Hidden Markov Model and tracks changes in noise characteristics by a sequential model update procedure that tracks the noise characteristics during the absence of speech. In addition the algorithm will when necessary create new model states to represent novel noise spectra and will merge existing states that have similar characteristics. We then extend our work in robust noise estimation during speech activity by incorporating a speech model into our existing noise model. The noise characteristics within each state are updated based on a speech presence probability which is derived from a modified Minima controlled recursive averaging method. We have demonstrated the effectiveness of our noise HMM in tracking both stationary and highly non-stationary noise, and shown that it gives improved performance over other conventional noise estimation methods when it is incorporated into a standard speech enhancement algorithm

Essays in Information Economics

This dissertation is composed of three essays considering the role of private information in economic environments. The first essay considers efficient investments into technologies such as auditing and enforcement systems that are designed to mitigate information and enforcement frictions that impede the provision of first best insurance against income risk. In the model, the principal can choose a level of enforceability that inhibits an agent\u27s ability to renege on the contract and a level of auditing that inhibits his ability to conceal income. The dynamics of the optimal contract imply an endogenous lower bound on the lifetime utility of an agent, strictly positive auditing at all points in the contract and positive enforcement only when the agent\u27s utility is sufficiently low. Furthermore, the two technologies operate as complements and substitutes at alternative points in the state space. The second essay considers a planning problem with hidden actions and hidden states where the component of utility affected by the unobservable state is separable from component governed by the hidden action. I show how this problem can be written recursively with a one dimensional state variable representing a modified version of the continuation utility promise. I apply the framework to a model in which an agent takes an unobservable decision to invest in human capital using resources allocated to him by the planner. Unlike similar environments without physical investment, it is shown numerically the immiserising does not necessarily hold. In the third essay, with Kyungmin Kim, I examine the effects of commitment on information transmission. We study and compare the behavioral consequences of honesty and white lie in communication. An honest agent is committed to telling the truth, while a white liar may manipulate information but only for the sake of the principal. We identify the effects of honesty and white lie on communication and show that the principal is often better off with a possibly honest agent than with a potential white liar. This result provides a fundamental rationale on why honesty is thought to be an important virtue in many contexts

Neural Network Operations and Susuki-Trotter evolution of Neural Network States

It was recently proposed to leverage the representational power of artificial neural networks, in particular Restricted Boltzmann Machines, in order to model complex quantum states of many-body systems [Science, 355(6325), 2017]. States represented in this way, called Neural Network States (NNSs), were shown to display interesting properties like the ability to efficiently capture long-range quantum correlations. However, identifying an optimal neural network representation of a given state might be challenging, and so far this problem has been addressed with stochastic optimization techniques. In this work we explore a different direction. We study how the action of elementary quantum operations modifies NNSs. We parametrize a family of many body quantum operations that can be directly applied to states represented by Unrestricted Boltzmann Machines, by just adding hidden nodes and updating the network parameters. We show that this parametrization contains a set of universal quantum gates, from which it follows that the state prepared by any quantum circuit can be expressed as a Neural Network State with a number of hidden nodes that grows linearly with the number of elementary operations in the circuit. This is a powerful representation theorem (which was recently obtained with different methods) but that is not directly useful, since there is no general and efficient way to extract information from this unrestricted description of quantum states. To circumvent this problem, we propose a step-wise procedure based on the projection of Unrestricted quantum states to Restricted quantum states. In turn, two approximate methods to perform this projection are discussed. In this way, we show that it is in principle possible to approximately optimize or evolve Neural Network States without relying on stochastic methods such as Variational Monte Carlo, which are computationally expensive

Representing Conversations for Scalable Overhearing

Open distributed multi-agent systems are gaining interest in the academic community and in industry. In such open settings, agents are often coordinated using standardized agent conversation protocols. The representation of such protocols (for analysis, validation, monitoring, etc) is an important aspect of multi-agent applications. Recently, Petri nets have been shown to be an interesting approach to such representation, and radically different approaches using Petri nets have been proposed. However, their relative strengths and weaknesses have not been examined. Moreover, their scalability and suitability for different tasks have not been addressed. This paper addresses both these challenges. First, we analyze existing Petri net representations in terms of their scalability and appropriateness for overhearing, an important task in monitoring open multi-agent systems. Then, building on the insights gained, we introduce a novel representation using Colored Petri nets that explicitly represent legal joint conversation states and messages. This representation approach offers significant improvements in scalability and is particularly suitable for overhearing. Furthermore, we show that this new representation offers a comprehensive coverage of all conversation features of FIPA conversation standards. We also present a procedure for transforming AUML conversation protocol diagrams (a standard human-readable representation), to our Colored Petri net representation