Speech Transmission Index from running speech : a neural network approach

Speech Transmission Index (STI) is an important objective parameter concerning speech intelligibility for sound transmission channels. It is normally measured with specific test signals to ensure high accuracy and good repeatability. Measurement with running speech was previously proposed, but accuracy is compromised and hence applications limited. A new approach that uses artificial neural networks to accurately extract the STI from received running speech is developed in this paper. Neural networks are trained on a large set of transmitted speech examples with prior knowledge of the transmission channels' STIs. The networks perform complicated nonlinear function mappings and spectral feature memorization to enable accurate objective parameter extraction from transmitted speech. Validations via simulations demonstrate the feasibility of this new method on a one-net-one-speech extract basis. In this case, accuracy is comparable with normal measurement methods. This provides an alternative to standard measurement techniques, and it is intended that the neural network method can facilitate occupied room acoustic measurements

Available transfer capability calculation with post-contingency generation rescheduling/load curtailment

The available transfer capability (ATC) is an important index indicating the remaining transfer capability in the physical transmission network for further commercial activity above existing commitments. In this paper, ATC mathematical model considering post-contingency generation rescheduling and load curtailment is first formulated. Benders decomposition method is then used to partition the ATC model above into a base case master problem and a series of independent subproblems relevant to various contingencies. Finally, an improved parallel solution scheme is employed to improve the convergence. Numerical results on a 4-bus test system show clearly the effectiveness of the presented method and necessity of considering post-contingency generation rescheduling and load curtailment in calculating ATC.published_or_final_versio

Communication requirements for risk-limiting dispatch in smart grid

The existing power grid infrastructures in many countries are primarily based on technologies that have been developed as centralized systems in which power is generated at major power plants and distributed to consumers through transmission and distribution lines. In the recent decade, with the increasing penetration of renewable energy sources such as solar and wind power, and smart electrical appliances, the centralized model may no longer hold, and the supply and demand for electricity become more dynamic. Moreover, the latest developed information and communication technologies (ICT) and power electronic technologies could enhance the efficiency and performance of power system operations. Recently, concerns with global warming have prompted many countries to announce research programs on smart grid, which is the transformation of the traditional electric power grid into an energy-efficient and environmentally friendly grid by the integration of ICT, power electronic, storage and control technologies. With the smart grid, there is an opportunity for a new operating paradigm that recognizes the changing structures of the power grid with renewable generation, and the high-resolution data, high speed communications, and high performance computation available with the advanced information infrastructure. A new operating paradigm, namely, risk-limiting dispatch, is proposed for the smart grid in this paper. In addition, we have identified the requirements of a communication infrastructure to support this new operating paradigm. ©2010 IEEE.published_or_final_versionThe IEEE International Conference on Communications Workshops (ICC 2010), Capetown, South Africa, 23-27 May 2010. In Proceedings of ICC, 2010, p. 1-

Efficient Euclidean projections onto the intersection of norm balls

Using sparse-inducing norms to learn robust models has received increasing attention from many fields for its attractive properties. Projection-based methods have been widely applied to learning tasks constrained by such norms. As a key building block of these methods, an efficient operator for Euclidean projection onto the intersection of ℓ 1 and ℓ 1,q norm balls (q = 2 or ∞) is proposed in this paper. We prove that the projection can be reduced to finding the root of an auxiliary function which is piecewise smooth and monotonic. Hence, a bisection algorithm is sufficient to solve the problem. We show that the time complexity of our solution is O(n + g log g) for q = 2 and O(n log n) for q = ∞), where n is the dimensionality of the vector to be projected and g is the number of disjoint groups; we confirm this complexity by experimentation. Empirical study reveals that our method achieves significantly better performance than classical methods in terms of running time and memory usage. We further show that embedded with our efficient projection operator, projection-based algorithms can solve regression problems with composite norm constraints more efficiently than other methods and give superior accuracy. Copyright 2012 by the author(s)/owner(s).postprintThe 29th International Conference on Machine Learning (ICML 2012). Edinburgh, Scotland, UK., 27 June-3 July 2012 In Proceedings of the 29th International Conference on Machine Learning, ICML-12, 2012, v. 1, p. 433-44

Machine learning and DSP algorithms for screening of possible osteoporosis using electronic stethoscopes

Osteoporosis is a prevalent but asymptomatic condition that affects a large population of the elderly, resulting in a high risk of fracture. Several methods have been developed and are available in general hospitals to indirectly assess the bone quality in terms of mineral material level and porosity. In this paper we describe a new method that uses a medical reflex hammer to exert testing stimuli, an electronic stethoscope to acquire impulse responses from tibia, and intelligent signal processing based on artificial neural network machine learning to determine the likelihood of osteoporosis. The proposed method makes decisions from the key components found in the time-frequency domain of impulse responses. Using two common pieces of clinical apparatus, this method might be suitable for the large population screening tests for the early diagnosis of osteoporosis, thus avoiding secondary complications. Following some discussions of the mechanism and procedure, this paper details the techniques of impulse response acquisition using a stethoscope and the subsequent signal processing and statistical machine learning algorithms for decision making. Pilot testing results achieved over 80% in detection sensitivity

Detection of osteoporosis from percussion responses using an electronic stethoscope and machine learning

Osteoporosis is an asymptomatic bone condition that affects a large proportion of the elderly population around the world, resulting in increased bone fragility and increased risk of fracture. Previous studies had shown that the vibroacoustic response of bone can indicate the quality of the bone condition. Therefore, the aim of the authors' project is to develop a new method to exploit this phenomenon to improve detection of osteoporosis in individuals. In this paper a method is described that uses a reflex hammer to exert testing stimuli on a patient's tibia and an electronic stethoscope to acquire the impulse responses. The signals are processed as mel frequency cepstrum coefficients and passed through an artificial neural network to determine the likelihood of osteoporosis from the tibia's impulse responses. Following some discussions of the mechanism and procedure, this paper details the signal acquisition using the stethoscope and the subsequent signal processing and the statistical machine learning algorithm. Pilot testing with 12 patients achieved over 80% sensitivity with a false positive rate below 30% and accuracies in the region of 70%. An extended dataset of 110 patients achieved an error rate of 30% with some room for improvement in the algorithm. By using common clinical apparatus and strategic machine learning, this method might be suitable as a large population screening test for the early diagnosis of osteoporosis, thus avoiding secondary complications

Automatic Speaker Recognition System in Adverse Conditions — Implication of Noise and Reverberation on System Performance

Speaker recognition has been developed and evolved over the past few decades into a supposedly mature technique. Existing methods typically utilize robust features extracted from clean speech. In real-world applications, especially security and forensics related ones, reliability of recognition becomes crucial, meanwhile limited speech samples and adverse acoustic conditions, most notably noise and reverberation, impose further complications. This paper is presented from a study into the behavior of typical speaker recognition systems in adverse retrieval phases. Following a brief review, a speaker recognition system was implemented using the MSR Identity Toolbox by Microsoft. Validation tests were carried out with clean speech and the speech contaminated by noise and/or reverberation of varying degrees. The image source method was adopted to take into account real acoustic conditions in the spaces. Statistical relationships between recognition accuracy and signal to noise ratios or reverberation times have therefore been established. Results show noise and reverberation can, to different extents, degrade the performance of recognition. Both reverberation time and direct to reverberation ratio can affect recognition accuracy. The findings may be used to estimate the accuracy of speaker recognition and further determine the likelihood a particular speaker