RawNet: Fast End-to-End Neural Vocoder

Neural networks based vocoders have recently demonstrated the powerful ability to synthesize high quality speech. These models usually generate samples by conditioning on some spectrum features, such as Mel-spectrum. However, these features are extracted by using speech analysis module including some processing based on the human knowledge. In this work, we proposed RawNet, a truly end-to-end neural vocoder, which use a coder network to learn the higher representation of signal, and an autoregressive voder network to generate speech sample by sample. The coder and voder together act like an auto-encoder network, and could be jointly trained directly on raw waveform without any human-designed features. The experiments on the Copy-Synthesis tasks show that RawNet can achieve the comparative synthesized speech quality with LPCNet, with a smaller model architecture and faster speech generation at the inference step.Comment: Submitted to Interspeech 2019, Graz, Austri

Domestic energy system operation with PV and V2G, to minimise running cost, and provide passive grid support

Today, the number of installed domestic PV systems is increasing, and more efficient appliances are used in households. This trend is a good signal for the UK Government, which has set a target to cut the UK CO2 emissions by 80% from 1990 levels before 2050. Further legislation was passed in 2019 to amend the target to 100% by 2050, to reach net zero emissions. However, with the increasing PV penetration, the level and direction of power flow on the UK electricity grid is less predictable, which brings challenges to current power grid and energy suppliers. Smart grid technology can solve the drawbacks of the PV penetration, and is the subject of significant investigation. In this thesis, hourly load profile modelling is introduced as the basis of the research, then a PV generation profile is determined for each typical size of PV system installed in the UK. An evaluation of the combined load and PV profiles throughout a year is carried out to address the sizing of an additional battery energy storage system. This facilitates an integrated domestic energy storage facility with renewable energy source, in order to create a win-win situation for both the customers and the grid. By using PV as an alternative energy source to power the home appliances, the system can reduce the dependence of household on grid energy, and it can shave the peak grid demand by managing the loads and exporting PV overproduction back to grid. Hence, the system can cut the electricity bill for customers and make profit by selling electricity to grid. The electricity tariff is considered when calculating the annual profit available, and conducting the system payback period for performance analysis. EV integration to the household in the form of vehicle to grid (V2G) is then examined based on the models developed. The complete domestic energy system model, including photovoltaic (PV) panels, battery energy storage system (BESS) and electric vehicle (EV) is updated to evaluate the impact of the V2G on the payback periods for a consumer. With a series of control algorithms applied, along with possible electricity tariffs, minimum energy usage and relative payback period for each variation of PV and BESS sizes within a proposed system are calculated. A battery ageing model is then developed to consider the annual battery degradation cost. It is shown that an EV can be used as extended stationary energy storage, together with a household BESS, to enhance the overall system performance

Exploring Timbre Disentanglement in Non-Autoregressive Cross-Lingual Text-to-Speech

In this paper, we present a FastPitch-based non-autoregressive cross-lingual Text-to-Speech (TTS) model built with language independent input representation and monolingual force aligners. We propose a phoneme length regulator that solves the length mismatch problem between language-independent phonemes and monolingual alignment results. Our experiments show that (1) an increasing number of training speakers encourages non-autoregressive cross-lingual TTS model to disentangle speaker and language representations, and (2) variance adaptors of FastPitch model can help disentangle speaker identity from learned representations in cross-lingual TTS. The subjective evaluation shows that our proposed model is able to achieve decent speaker consistency and similarity. We further improve the naturalness of Mandarin-dominated mixed-lingual utterances by utilizing the controllability of our proposed model.Comment: Submitted to ICASSP 202

Gaussian Max-Value Entropy Search for Multi-Agent Bayesian Optimization

We study the multi-agent Bayesian optimization (BO) problem, where multiple agents maximize a black-box function via iterative queries. We focus on Entropy Search (ES), a sample-efficient BO algorithm that selects queries to maximize the mutual information about the maximum of the black-box function. One of the main challenges of ES is that calculating the mutual information requires computationally-costly approximation techniques. For multi-agent BO problems, the computational cost of ES is exponential in the number of agents. To address this challenge, we propose the Gaussian Max-value Entropy Search, a multi-agent BO algorithm with favorable sample and computational efficiency. The key to our idea is to use a normal distribution to approximate the function maximum and calculate its mutual information accordingly. The resulting approximation allows queries to be cast as the solution of a closed-form optimization problem which, in turn, can be solved via a modified gradient ascent algorithm and scaled to a large number of agents. We demonstrate the effectiveness of Gaussian max-value Entropy Search through numerical experiments on standard test functions and real-robot experiments on the source-seeking problem. Results show that the proposed algorithm outperforms the multi-agent BO baselines in the numerical experiments and can stably seek the source with a limited number of noisy observations on real robots.Comment: 10 pages, 9 figure

Treatment of hemimasticatory spasm secondary to parry-romberg syndrome via partial resection of the trigeminal nerve motor branch under intraoperative neurophysiological monitoring: A case report and literature review

Parry-Romberg syndrome (PRS) combined with hemimasticatory spasm (HMS) is a rare craniofacial disorder characterized by unilateral facial tissue atrophy with paroxysmal involuntary contractions of the jaw-closing muscles. Although a majority believe that this is a result of demyelination changes from the effect of the facial involvement of PRS on the trigeminal nerve motor branches, the mechanism of PRS is presently unclear. Moreover, the therapeutic effects of existing drugs that target PRS have not been satisfactory. For intolerable spasms of the masticatory muscles, botulinum toxin injection may temporarily relieve the symptoms of spasms. We report a case of HMS secondary to PRS that was treated via a partial resection of the trigeminal nerve motor branch under intraoperative neurophysiological monitoring

Reducing toxicity and increasing efficiency: aconitine with liquiritin and glycyrrhetinic acid regulate calcium regulatory proteins in rat myocardial cell

Background: Compatibility of Radix Aconiti Carmichaeli and Liquorice is known to treat heart diseases such as heart failure and cardiac arrhythmias. This work answers the question that whether the active components (Aconitine, Liquiritin and Glycyrrhetinic Acid) of Radix Aconiti Carmichaeli and Liquorice could result in regulating intracellular calcium homeostasis and calcium cycling, and thereby verifies the therapeutic material basis.Materials and Methods: The myocardial cells were divided into twelve groups randomly as control group, Aconitine group, nine different dose groups that orthogonal combined with Aconitine, Liquiritin and Glycyrrhetinic Acid, and Verapamil group. The myocardial cellular survival rate and morphology were assessed. The expression of calcium regulation protein(RyR2、NCX1、DHPR-a1) in the myocardial cell by Western-blotting.Results: The results exhibited that Aconitine (120 uM) significantly damaged on myocardial cell, decreased the survival rate and expression of Na+/Ca2+ exchangers (NCX1) and dihydropteridine reducta-α1 (DHPR-a1), and increased the expression of ryanodine receptor type2 (RyR2) obviously. The compatibility groups (Aconitine, Liquiritin and Glycyrrhetinic Acid) all could against the damage on the myocardial cell by Aconitine at different levels.Conclusion: Aconitine with Liquiritin and Glycyrrhetinic Acid may regulate the expression of calcium-regulated proteins to protect myocardial cells from damage.Keywords: Aconitine, Liquiritin, Glycyrrhetinic Acid, myocardial cell, calcium regulator