Robust Personal Audio Geometry Optimization in the SVD-Based Modal Domain

© 2014 IEEE. Personal audio generates sound zones in a shared space to provide private and personalized listening experiences with minimized interference between consumers. Regularization has been commonly used to increase the robustness of such systems against potential perturbations in the sound reproduction. However, the performance is limited by the system geometry such as the number and location of the loudspeakers and controlled zones. This paper proposes a geometry optimization method to find the most geometrically robust approach for personal audio amongst all available candidate system placements. The proposed method aims to approach the most 'natural' sound reproduction so that the solo control of the listening zone coincidently accompanies the preferred quiet zone. Being formulated in the SVD-based modal domain, the method is demonstrated by applications in three typical personal audio optimizations, i.e., the acoustic contrast control, the pressure matching, and the planarity control. Simulation results show that the proposed method can obtain the system geometry with better avoidance of 'occlusion,' improved robustness to regularization, and improved broadband equalization

An experimental study on transfer function estimation using acoustic modelling and singular value decomposition.

Transfer functions relating sound source strengths and the sound pressure at field points are important for sound field control. Recently, two modal domain methods for transfer function estimation have been compared using numerical simulations. One is the spatial harmonic decomposition (SHD) method, which models a sound field with a series of cylindrical waves; while the other is the singular value decomposition (SVD) method, which uses prior sound source location information to build an acoustic model and obtain basis functions for sound field modelling. In this paper, the feasibility of the SVD method using limited measurements to estimate transfer functions over densely spaced field samples within a target region is demonstrated experimentally. Experimental results with various microphone placements and system configurations are reported to demonstrate the geometric flexibility of the SVD method compared to the SHD method. It is shown that the SVD method can estimate broadband transfer functions up to 3099 Hz for a target region with a radius of 0.083 m using three microphones, and allow flexibility in system geometry. Furthermore, an application example of acoustic contrast control is presented, showing that the proposed method is a promising approach to facilitating broadband sound zone control with limited microphones

Comparison of DCT, SVD and BFOA based multimodal biometric watermarking systems

AbstractDigital image watermarking is a major domain for hiding the biometric information, in which the watermark data are made to be concealed inside a host image imposing imperceptible change in the picture. Due to the advance in digital image watermarking, the majority of research aims to make a reliable improvement in robustness to prevent the attack. The reversible invisible watermarking scheme is used for fingerprint and iris multimodal biometric system. A novel approach is used for fusing different biometric modalities. Individual unique modalities of fingerprint and iris biometric are extracted and fused using different fusion techniques. The performance of different fusion techniques is evaluated and the Discrete Wavelet Transform fusion method is identified as the best. Then the best fused biometric template is watermarked into a cover image. The various watermarking techniques such as the Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD) and Bacterial Foraging Optimization Algorithm (BFOA) are implemented to the fused biometric feature image. Performance of watermarking systems is compared using different metrics. It is found that the watermarked images are found robust over different attacks and they are able to reverse the biometric template for Bacterial Foraging Optimization Algorithm (BFOA) watermarking technique

Machine Learning Models for Educational Platforms

Scaling up education online and onlife is presenting numerous key challenges, such as hardly manageable classes, overwhelming content alternatives, and academic dishonesty while interacting remotely. However, thanks to the wider availability of learning-related data and increasingly higher performance computing, Artificial Intelligence has the potential to turn such challenges into an unparalleled opportunity. One of its sub-fields, namely Machine Learning, is enabling machines to receive data and learn for themselves, without being programmed with rules. Bringing this intelligent support to education at large scale has a number of advantages, such as avoiding manual error-prone tasks and reducing the chance that learners do any misconduct. Planning, collecting, developing, and predicting become essential steps to make it concrete into real-world education. This thesis deals with the design, implementation, and evaluation of Machine Learning models in the context of online educational platforms deployed at large scale. Constructing and assessing the performance of intelligent models is a crucial step towards increasing reliability and convenience of such an educational medium. The contributions result in large data sets and high-performing models that capitalize on Natural Language Processing, Human Behavior Mining, and Machine Perception. The model decisions aim to support stakeholders over the instructional pipeline, specifically on content categorization, content recommendation, learners’ identity verification, and learners’ sentiment analysis. Past research in this field often relied on statistical processes hardly applicable at large scale. Through our studies, we explore opportunities and challenges introduced by Machine Learning for the above goals, a relevant and timely topic in literature. Supported by extensive experiments, our work reveals a clear opportunity in combining human and machine sensing for researchers interested in online education. Our findings illustrate the feasibility of designing and assessing Machine Learning models for categorization, recommendation, authentication, and sentiment prediction in this research area. Our results provide guidelines on model motivation, data collection, model design, and analysis techniques concerning the above applicative scenarios. Researchers can use our findings to improve data collection on educational platforms, to reduce bias in data and models, to increase model effectiveness, and to increase the reliability of their models, among others. We expect that this thesis can support the adoption of Machine Learning models in educational platforms even more, strengthening the role of data as a precious asset. The thesis outputs are publicly available at https://www.mirkomarras.com

Large-Scale Video Event Detection

Because of the rapid growth of large scale video recording and sharing, there is a growing need for robust and scalable solutions for analyzing video content. The ability to detect and recognize video events that capture real-world activities is one of the key and complex problems. This thesis aims at the development of robust and efficient solutions for large scale video event detection systems. In particular, we investigate the problem in two areas: first, event detection with automatically discovered event specific concepts with organized ontology, and second, event detection with multi-modality representations and multi-source fusion. Existing event detection works use various low-level features with statistical learning models, and achieve promising performance. However, such approaches lack the capability of interpreting the abundant semantic content associated with complex video events. Therefore, mid-level semantic concept representation of complex events has emerged as a promising method for understanding video events. In this area, existing works can be categorized into two groups: those that manually define a specialized concept set for a specific event, and those that apply a general concept lexicon directly borrowed from existing object, scene and action concept libraries. The first approach seems to require tremendous manual efforts, whereas the second approach is often insufficient in capturing the rich semantics contained in video events. In this work, we propose an automatic event-driven concept discovery method, and build a large-scale event and concept library with well-organized ontology, called EventNet. This method is different from past work that applies a generic concept library independent of the target while not requiring tedious manual annotations. Extensive experiments over the zero-shot event retrieval task when no training samples are available show that the proposed EventNet library consistently and significantly outperforms the state-of-the-art methods. Although concept-based event representation can interpret the semantic content of video events, in order to achieve high accuracy in event detection, we also need to consider and combine various features of different modalities and/or across different levels. One one hand, we observe that joint cross-modality patterns (e.g., audio-visual pattern) often exist in videos and provide strong multi-modal cues for detecting video events. We propose a joint audio-visual bi-modal codeword representation, called bi-modal words, to discover cross-modality correlations. On the other hand, combining features from multiple sources often produces performance gains, especially when the features complement with each other. Existing multi-source late fusion methods usually apply direct combination of confidence scores from different sources. This becomes limiting because heterogeneous results from various sources often produce incomparable confidence scores at different scales. This makes direct late fusion inappropriate, thus posing a great challenge. Based upon the above considerations, we propose a robust late fusion method with rank minimization, that not only achieves isotonicity among various scores from different sources, but also recovers a robust prediction score for individual test samples. We experimentally show that the proposed multi-modality representation and multi-source fusion methods achieve promising results compared with other benchmark baselines. The main contributions of the thesis include the following. 1. Large scale event and concept ontology: a) propose an automatic framework for discovering event-driven concepts; b) build the largest video event ontology, EventNet, which includes 500 complex events and 4,490 event-specific concepts; c) build the first interactive system that allows users to explore high-level events and associated concepts in videos with event browsing, search, and tagging functions. 2. Event detection with multi-modality representations and multi-source fusion: a) propose novel bi-modal codeword construction for discovering multi-modality correlations; b) propose novel robust late fusion with rank minimization method for combining information from multiple sources. The two parts of the thesis are complimentary. Concept-based event representation provides rich semantic information for video events. Cross-modality features also provide complementary information from multiple sources. The combination of those two parts in a unified framework can offer great potential for advancing state-of-the-art in large-scale event detection

Machine Learning Models to automate Radiotherapy Structure Name Standardization

Structure name standardization is a critical problem in Radiotherapy planning systems to correctly identify the various Organs-at-Risk, Planning Target Volumes and `Other\u27 organs for monitoring present and future medications. Physicians often label anatomical structure sets in Digital Imaging and Communications in Medicine (DICOM) images with nonstandard random names. Hence, the standardization of these names for the Organs at Risk (OARs), Planning Target Volumes (PTVs), and `Other\u27 organs is a vital problem. Prior works considered traditional machine learning approaches on structure sets with moderate success. We compare both traditional methods and deep neural network-based approaches on the multimodal vision-language prostate cancer patient data, compiled from the radiotherapy centers of the US Veterans Health Administration (VHA) and Virginia Commonwealth University (VCU) for structure name standardization. These de-identified data comprise 16,290 prostate structures. Our method integrates the multimodal textual and imaging data with Convolutional Neural Network (CNN)-based deep learning approaches such as CNN, Visual Geometry Group (VGG) network, and Residual Network (ResNet) and shows improved results in prostate radiotherapy structure name standardization. Our proposed deep neural network-based approach on the multimodal vision-language prostate cancer patient data provides state-of-the-art results for structure name standardization. Evaluation with macro-averaged F1 score shows that our CNN model with single-modal textual data usually performs better than previous studies. We also experimented with various combinations of multimodal data (masked images, masked dose) besides textual data. The models perform well on textual data alone, while the addition of imaging data shows that deep neural networks achieve better performance using information present in other modalities. Our pipeline can successfully standardize the Organs-at-Risk and the Planning Target Volumes, which are of utmost interest to the clinicians and simultaneously, performs very well on the `Other\u27 organs. We performed comprehensive experiments by varying input data modalities to show that using masked images and masked dose data with text outperforms the combination of other input modalities. We also undersampled the majority class, i.e., the `Other\u27 class, at different degrees and conducted extensive experiments to demonstrate that a small amount of majority class undersampling is essential for superior performance. Overall, our proposed integrated, deep neural network-based architecture for prostate structure name standardization can solve several challenges associated with multimodal data. The VGG network on the masked image-dose data combined with CNNs on the text data performs the best and presents the state-of-the-art in this domain

Computational Multimedia for Video Self Modeling

Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of oneself. This is the idea behind the psychological theory of self-efficacy - you can learn or model to perform certain tasks because you see yourself doing it, which provides the most ideal form of behavior modeling. The effectiveness of VSM has been demonstrated for many different types of disabilities and behavioral problems ranging from stuttering, inappropriate social behaviors, autism, selective mutism to sports training. However, there is an inherent difficulty associated with the production of VSM material. Prolonged and persistent video recording is required to capture the rare, if not existed at all, snippets that can be used to string together in forming novel video sequences of the target skill. To solve this problem, in this dissertation, we use computational multimedia techniques to facilitate the creation of synthetic visual content for self-modeling that can be used by a learner and his/her therapist with a minimum amount of training data. There are three major technical contributions in my research. First, I developed an Adaptive Video Re-sampling algorithm to synthesize realistic lip-synchronized video with minimal motion jitter. Second, to denoise and complete the depth map captured by structure-light sensing systems, I introduced a layer based probabilistic model to account for various types of uncertainties in the depth measurement. Third, I developed a simple and robust bundle-adjustment based framework for calibrating a network of multiple wide baseline RGB and depth cameras