Computational Modelling and Analysis of Vibrato and Portamento in Expressive Music Performance

PhD, 148ppVibrato and portamento constitute two expressive devices involving continuous pitch modulation and is widely employed in string, voice, wind music instrument performance. Automatic extraction and analysis of such expressive features form some of the most important aspects of music performance research and represents an under-explored area in music information retrieval. This thesis aims to provide computational and scalable solutions for the automatic extraction and analysis of performed vibratos and portamenti. Applications of the technologies include music learning, musicological analysis, music information retrieval (summarisation, similarity assessment), and music expression synthesis. To automatically detect vibratos and estimate their parameters, we propose a novel method based on the Filter Diagonalisation Method (FDM). The FDM remains robust over short time frames, allowing frame sizes to be set at values small enough to accurately identify local vibrato characteristics and pinpoint vibrato boundaries. For the determining of vibrato presence, we test two alternate decision mechanisms—the Decision Tree and Bayes’ Rule. The FDM systems are compared to state-of-the-art techniques and obtains the best results. The FDM’s vibrato rate accuracies are above 92.5%, and the vibrato extent accuracies are about 85%. We use the Hidden Markov Model (HMM) with Gaussian Mixture Model (GMM) to detect portamento existence. Upon extracting the portamenti, we propose a Logistic Model for describing portamento parameters. The Logistic Model has the lowest root mean squared error and the highest adjusted Rsquared value comparing to regression models employing Polynomial and Gaussian functions, and the Fourier Series. The vibrato and portamento detection and analysis methods are implemented in AVA, an interactive tool for automated detection, analysis, and visualisation of vibrato and portamento. Using the system, we perform crosscultural analyses of vibrato and portamento differences between erhu and violin performance styles, and between typical male or female roles in Beijing opera singing

Distributed Multi-Time Slot Power Balancing Control of Power Systems with Energy Storage Devices

This paper studies a crucial problem in power system balancing control, i.e., the multi-time slot economic dispatch (MTSED) problem, for power grids with substantial renewables, synchronous generators (SGs), and energy storage devices (ESDs). The target of MTSED is to optimally coordinate active/reactive power outputs of all controllable units to meet a forecast net demand profile over multiple time slots within a receding finite time horizon. Firstly, the MTSED is formulated as an optimization problem with operational constraints, including the limits on the output of each controllable unit, ramping rates of SGss, energy levels of ESDs, and bus voltages. Then, a novel projection-based algorithm is developed to solve the problem in a distributed way. In particular, the distributed algorithm is not limited to solving the MTSED problem but also applies to more general optimization problems with both generic convex objective functions and hard feasibility constraints. Finally, case studies verify the effectiveness of the proposed method

Energy Loss from Transient Eddies due to Lee Wave Generation in the Southern Ocean

Observations suggest that enhanced turbulent dissipation and mixing over rough topography are modulated by the transient eddy field through the generation and breaking of lee waves in the Southern Ocean. Idealized simulations also suggest that lee waves are important in the energy pathway from eddies to turbulence. However, the energy loss from eddies due to lee wave generation remains poorly estimated. This study quantifies the relative energy loss from the time-mean and transient eddy flow in the Southern Ocean due to lee wave generation using an eddy-resolving global ocean model and three independent topographic datasets. The authors find that the energy loss from the transient eddy flow (0.12 TW; 1 TW = 1012 W) is larger than that from the time-mean flow (0.04 TW) due to lee wave generation; lee wave generation makes a larger contribution (0.12 TW) to the energy loss from the transient eddy flow than the dissipation in turbulent bottom boundary layer (0.05 TW). This study also shows that the energy loss from the time-mean flow is regulated by the transient eddy flow, and energy loss from the transient eddy flow is sensitive to the representation of anisotropy in small-scale topography. It is implied that lee waves should be parameterized in eddy-resolving global ocean models to improve the energetics of resolved flow.This research was undertaken on the NCI National Facility in Canberra, Australia, which is supported by the Australian Government. LY was supported by the joint CSIRO–UTAS QMS program. MN was supported by the Australian Research Council (ARC) Discovery Early Career Research Award (DECRA) Fellowship (DE150100937)

Dense RGB SLAM with Neural Implicit Maps

There is an emerging trend of using neural implicit functions for map representation in Simultaneous Localization and Mapping (SLAM). Some pioneer works have achieved encouraging results on RGB-D SLAM. In this paper, we present a dense RGB SLAM method with neural implicit map representation. To reach this challenging goal without depth input, we introduce a hierarchical feature volume to facilitate the implicit map decoder. This design effectively fuses shape cues across different scales to facilitate map reconstruction. Our method simultaneously solves the camera motion and the neural implicit map by matching the rendered and input video frames. To facilitate optimization, we further propose a photometric warping loss in the spirit of multi-view stereo to better constrain the camera pose and scene geometry. We evaluate our method on commonly used benchmarks and compare it with modern RGB and RGB-D SLAM systems. Our method achieves favorable results than previous methods and even surpasses some recent RGB-D SLAM methods. Our source code will be publicly available.Comment: Accepted by ICLR 2023; Pre-Camera-Ready Versio

Inhibition of cyclin-dependent kinase 7 down-regulates yes-associated protein expression in mesothelioma cells.

Cyclin-dependent kinase 7 (CDK7) is a protein kinase that plays a major role in transcription initiation. Yes-associated protein (YAP) is a main effector of the Hippo/YAP signalling pathway. Here, we investigated the role of CDK7 on YAP regulation in human malignant pleural mesothelioma (MPM). We found that in microarray samples of human MPM tissue, immunohistochemistry staining showed correlation between the expression level of CDK7 and YAP (n = 70, r = .513). In MPM cells, CDK7 expression level was significantly correlated with GTIIC reporter activity (r = .886, P = .019). Inhibition of CDK7 by siRNA decreased the YAP protein level and the GTIIC reporter activity in the MPM cell lines 211H, H290 and H2052. Degradation of the YAP protein was accelerated after CDK7 knockdown in 211H, H290 and H2052 cells. Inhibition of CDK7 reduced tumour cell migration and invasion, as well as tumorsphere formation ability. Restoration of the CDK7 gene rescued the YAP protein level and GTIIC reporter activity after siRNA knockdown in 211H and H2052 cells. Finally, we performed a co-immunoprecipitation analysis using an anti-YAP antibody and captured the CDK7 protein in 211H cells. Our results suggest that CDK7 inhibition reduces the YAP protein level by promoting its degradation and suppresses the migration and invasion of MPM cells. Cyclin-dependent kinase 7 may be a promising therapeutic target for MPM