Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music

To model the periodicity of beats, state-of-the-art beat tracking systems use "post-processing trackers" (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo. For expressive classical music, however, these assumptions can be too rigid. With two large datasets of Western classical piano music, namely the Aligned Scores and Performances (ASAP) dataset and a dataset of Chopin's Mazurkas (Maz-5), we report on experiments showing the failure of existing PPTs to cope with local tempo changes, thus calling for new methods. In this paper, we propose a new local periodicity-based PPT, called predominant local pulse-based dynamic programming (PLPDP) tracking, that allows for more flexible tempo transitions. Specifically, the new PPT incorporates a method called "predominant local pulses" (PLP) in combination with a dynamic programming (DP) component to jointly consider the locally detected periodicity and beat activation strength at each time instant. Accordingly, PLPDP accounts for the local periodicity, rather than relying on a global tempo assumption. Compared to existing PPTs, PLPDP particularly enhances the recall values at the cost of a lower precision, resulting in an overall improvement of F1-score for beat tracking in ASAP (from 0.473 to 0.493) and Maz-5 (from 0.595 to 0.838).Comment: Accepted to IEEE/ACM Transactions on Audio, Speech, and Language Processing (July 2023

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

The Deep Space Network. An instrument for radio navigation of deep space probes

The Deep Space Network (DSN) network configurations used to generate the navigation observables and the basic process of deep space spacecraft navigation, from data generation through flight path determination and correction are described. Special emphasis is placed on the DSN Systems which generate the navigation data: the DSN Tracking and VLBI Systems. In addition, auxiliary navigational support functions are described

Improving Detection of Dim Targets: Optimization of a Moment-based Detection Algorithm

Wide area motion imagery (WAMI) sensor technology is advancing rapidly. Increases in frame rates and detector array sizes have led to a dramatic increase in the volume of data that can be acquired. Without a corresponding increase in analytical manpower, much of these data remain underutilized. This creates a need for fast, automated, and robust methods for detecting dim, moving signals of interest. Current approaches fall into two categories: detect-before-track (DBT) and track-before-detect (TBD) methods. The DBT methods use thresholding to reduce the quantity of data to be processed, making real time implementation practical but at the cost of the ability to detect low signal to noise ratio (SNR) targets without acceptance of a high false alarm rate. TBD methods exploit both the temporal and spatial information simultaneously to make detection of low SNR targets possible, but at the cost of computation time. This research seeks to contribute to the near real time detection of low SNR, unresolved moving targets through an extension of earlier work on higher order moments anomaly detection, a method that exploits both spatial and temporal information but is still computationally efficient and massively parallellizable. The MBD algorithm was found to detect targets comparably with leading TBD methods in 1000th the time

Noise-limited scene-change detection in images

This thesis describes the theoretical, experimental, and practical aspects of a noise-limited method for scene-change detection in images. The research is divided into three sections: noise analysis and modelling, dual illumination scene-change modelling, and integration of noise into the scene-change model. The sources of noise within commercially available digital cameras are described, with a new model for image noise derived for charge-coupled device (CCD) cameras. The model is validated experimentally through the development of techniques that allow the individual noise components to be measured from the analysis of output images alone. A generic model for complementary metal-oxide-semiconductor (CMOS) cameras is also derived. Methods for the analysis of spatial (inter-pixel) and temporal (intra-pixel) noise are developed. These are used subsequently to investigate the effects of environmental temperature on camera noise. Based on the cameras tested, the results show that the CCD camera noise response to variation in environmental temperature is complex whereas the CMOS camera response simply increases monotonically. A new concept for scene-change detection is proposed based upon a dual illumination concept where both direct and ambient illumination sources are present in an environment, such as that which occurs in natural outdoor scenes with direct sunlight and ambient skylight. The transition of pixel colour from the combined direct and ambient illuminants to the ambient illuminant only is modelled. A method for shadow-free scene-change is then developed that predicts a pixel's colour when the area in the scene is subjected to ambient illumination only, allowing pixel change to be distinguished as either being due to a cast shadow or due to a genuine change in the scene. Experiments on images captured in controlled lighting demonstrate 91% of scene-change and 83% of cast shadows are correctly determined from analysis of pixel colour change alone. A statistical method for detecting shadow-free scene-change is developed. This is achieved by bounding the dual illumination model by the confidence interval associated with the pixel's noise. Three benefits arise from the integration of noise into the scene-change detection method: - The necessity for pre-filtering images for noise is removed; - All empirical thresholds are removed; and - Performance is improved. The noise-limited scene-change detection algorithm correctly classifies 93% of scene-change and 87% of cast shadows from pixel colour change alone. When simple post-analysis size-filtering is applied both these figures increase to 95%

Holistic Temporal Situation Interpretation for Traffic Participant Prediction

For a profound understanding of traffic situations including a prediction of traf- fic participants’ future motion, behaviors and routes it is crucial to incorporate all available environmental observations. The presence of sensor noise and depen- dency uncertainties, the variety of available sensor data, the complexity of large traffic scenes and the large number of different estimation tasks with diverging requirements require a general method that gives a robust foundation for the de- velopment of estimation applications. In this work, a general description language, called Object-Oriented Factor Graph Modeling Language (OOFGML), is proposed, that unifies formulation of esti- mation tasks from the application-oriented problem description via the choice of variable and probability distribution representation through to the inference method definition in implementation. The different language properties are dis- cussed theoretically using abstract examples. The derivation of explicit application examples is shown for the automated driv- ing domain. A domain-specific ontology is defined which forms the basis for four exemplary applications covering the broad spectrum of estimation tasks in this domain: Basic temporal filtering, ego vehicle localization using advanced interpretations of perceived objects, road layout perception utilizing inter-object dependencies and finally highly integrated route, behavior and motion estima- tion to predict traffic participant’s future actions. All applications are evaluated as proof of concept and provide an example of how their class of estimation tasks can be represented using the proposed language. The language serves as a com- mon basis and opens a new field for further research towards holistic solutions for automated driving

Retrieving, annotating and recognizing human activities in web videos

Recent e orts in computer vision tackle the problem of human activity understanding in video sequences. Traditionally, these algorithms require annotated video data to learn models. In this work, we introduce a novel data collection framework, to take advantage of the large amount of video data available on the web. We use this new framework to retrieve videos of human activities, and build training and evaluation datasets for computer vision algorithms. We rely on Amazon Mechanical Turk workers to obtain high accuracy annotations. An agglomerative clustering technique brings the possibility to achieve reliable and consistent annotations for temporal localization of human activities in videos. Using two datasets, Olympics Sports and our novel Daily Human Activities dataset, we show that our collection/annotation framework can make robust annotations of human activities in large amount of video data

Study on Perception-Action Scheme for Human-Robot Musical Interaction in Wind Instrumental Play

制度:新 ; 報告番号:甲3337号 ; 学位の種類:博士(工学) ; 授与年月日:2011/2/25 ; 早大学位記番号:新564

Fundamental Experimental Studies In Scaling, Blast Mitigation and Material Processing

In this work, two separate research efforts are discussed. They include experimental studies in (1) Scaling and Blast Mitigation and (2) Scaling in Friction Stir Extrusion. In both studies, the primary experimental measurement method is three-dimensional digital image correlation (3D-DIC), a non-contacting full-field measurement method that is applicable for both high-rate loading and quasi-static loading conditions. Scaling and Blast Mitigation Studies: A series of properly scaled structures was subjected to buried blast loading conditions via detonation of a small explosive buried in saturated sand. Using high speed stereo-vision systems to record the deformations of selected regions on the upper surface of the structure, results clearly show that appropriate scaling of small specimens is adequate to compare responses from different size structures subjected to scaled levels of explosive loading, provided that the dominant physical processes remain the same in all cases. Upon completion of the basic scaling studies, small scale models representing key vehicle structural elements, including (a) floorboards and bottom-mounted, downward V-shaped hulls in various configurations; (b) steel frames and steel structures with various frame connections and coatings, were subjected to buried blast loading. The results were used to compare various geometrical designs, with the primary metrics for the comparisons being vertical acceleration and the Head Injury Criterion. Results from these studies show that personnel on typical floorboard structures during blast loading will incur unacceptable shock loading conditions, resulting in either serious or fatal injury. However, results also show that an appropriate design using frame-mounted passenger seating could reduce the potential for injury to an acceptable level. Scaling and Friction Stir Extrusion Studies: With the goal of tracking particles in a highly viscous, transparent fluid under conditions that approximate the same Reynold\u27s Number as expected in friction stir processing of a metallic material, a complete experimental apparatus was developed. Since 3D-DIC is used to track the particles within the viscous fluid during the flow and extrusion processes, software was written to account for the effects of refraction at the air-glass and glass-fluid interfaces so that the recorded image positions could be accurately converted to 3D locations within the fluid. Next, a series of baseline (known marker positions on rigid targets within the fluid) and extrusion experiments were performed. Results obtained from baseline experiments where the true positions of markers are known confirm that the method is quite accurate. Finally, through sparse seeding of the fluid with neutrally buoyant spherical particles, a series of rotational flow and extrusion experiments were performed. Rotational flow experimental results were in excellent agreement with simulations, while the extrusion data is in very good agreement with simulations in the latter part of the extrusion process