A computational framework for sound segregation in music signals

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Validity of the overclaiming technique as a method to account for response bias in self-assessment questions : analysis on the basis of the PISA 2012 data

The presented work is devoted to study the validity of overclaiming technique (OCT) as a measure of response (positivity) bias. Three main aims of the analyses performed were: a) assess methods' utility to enhance predictive validity of self-report by accounting for response biases, b) investigate proposed mechanisms of overclaiming, c) expand nomological network of the method by presenting a wide set of both individual-level and cluster-level (school) correlates. The obtained results pointed that OCT can be used in order to account for response biases in self-report data. Important differences regarding use and interpretation of the different OCT scoring systems were found and commented. Two systems, one based of signal detection theory (SDT), other on item response theory model (IRT), were proposed as viable scorings of OCT. Choice between them is not trivial as it influences results' interpretation and model specification. Three possible mechanisms of overclaiming were tested: a) motivated response bias (self-favouring bias, socially desirable responding), b) memory bias (overgeneralised knowledge or faulty memory control) and c) response styles and careless responding. The results pointed that all three mechanisms are probable and that overclaiming is most probably a heterogenous phenomenon of multiple causes. However, the analyses pointed out that one of the memory bias hypotheses, the overgeneralised knowledge account, does not hold and that there is much more evidence for the competitive metacognitive account. It is to said that overclaiming is at least partially attributable to insufficient monitoring of one's knowledge. Evidence for a relation between careless responding and overclaiming was also obtained, indicating that at least some of the overclaimed responses can be attributed due to inattentive responding. Obtained results on the relations between response styles and overclaiming were complicated; they warrant further studies as the results here probably greatly depend on the technical details of analysis, e.g. response style definition and coding adopted. The analysed cluster-level covariates demonstrated that only very limited portion of OCT variance can be ascribed to the school-level of analysis. Gender, socio-economic status and locus of control proved to be significantly related to overclaiming among the individual-level correlates assessed. Boys yielded higher overclaiming bias than girls and students of external locus of control were more biased in their self-reports in comparison to students of internal locus of control. The work comprises also analysis of the PISA's OCT latent structure. The results evidenced bifactor structure of the scale, with the general factor interpreted as math ability while the two specific factors were given a tentative explanation concentrated around item difficulty (one specific factor emerged for easy items, one for hard items). These findings point to a multi-dimensional character of OCT and a large role played by domain ability in OCT responding. Moreover, latent class analysis (LCA) performed identified an "overclaiming" group among the participants which was characterised by high overclaiming and unwarrantedly high self-report profile regarding math-related abilities and social life. However, this group counted only around 9% of the total sample. Implications of these findings are commented in the work, along with theoretical integration and ideas for future studies with the use of OCT

Realizing Software Defined Radio - A Study in Designing Mobile Supercomputers.

The physical layer of most wireless protocols is traditionally implemented in custom hardware to satisfy the heavy computational requirements while keeping power consumption to a minimum. These implementations are time consuming to design and difficult to verify. A programmable hardware platform capable of supporting software implementations of the physical layer, or Software Defined Radio (SDR), has a number of advantages. These include support for multiple protocols, faster time-to-market, higher chip volumes, and support for late implementation changes. The challenge is to achieve this under the power budget of a mobile device. Wireless communications belong to an emerging class of applications with the processing requirements of a supercomputer but the power constraints of a mobile device -- mobile supercomputing. This thesis presents a set of design proposals for building a programmable wireless communication solution. In order to design a solution that can meet the lofty requirements of SDR, this thesis takes an application-centric design approach -- evaluate and optimize all aspects of the design based on the characteristics of wireless communication protocols. This includes a DSP processor architecture optimized for wireless baseband processing, wireless algorithm optimizations, and language and compilation tool support for the algorithm software and the processor hardware. This thesis first analyzes the software characteristics of SDR. Based on the analysis, this thesis proposes the Signal-Processing On-Demand Architecture (SODA), a fully programmable multi-core architecture that can support the computation requirements of third generation wireless protocols, while operating within the power budget of a mobile device. This thesis then presents wireless algorithm implementations and optimizations for the SODA processor architecture. A signal processing language extension (SPEX) is proposed to help the software development efforts of wireless communication protocols on SODA-like multi-core architecture. And finally, the SPIR compiler is proposed to automatically map SPEX code onto the multi-core processor hardware.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61760/1/linyz_1.pd

Real-Time Localization Using Software Defined Radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system

Trennung und Schätzung der Anzahl von Audiosignalquellen mit Zeit- und Frequenzüberlappung

Everyday audio recordings involve mixture signals: music contains a mixture of instruments; in a meeting or conference, there is a mixture of human voices. For these mixtures, automatically separating or estimating the number of sources is a challenging task. A common assumption when processing mixtures in the time-frequency domain is that sources are not fully overlapped. However, in this work we consider some cases where the overlap is severe — for instance, when instruments play the same note (unison) or when many people speak concurrently ("cocktail party") — highlighting the need for new representations and more powerful models. To address the problems of source separation and count estimation, we use conventional signal processing techniques as well as deep neural networks (DNN). We first address the source separation problem for unison instrument mixtures, studying the distinct spectro-temporal modulations caused by vibrato. To exploit these modulations, we developed a method based on time warping, informed by an estimate of the fundamental frequency. For cases where such estimates are not available, we present an unsupervised model, inspired by the way humans group time-varying sources (common fate). This contribution comes with a novel representation that improves separation for overlapped and modulated sources on unison mixtures but also improves vocal and accompaniment separation when used as an input for a DNN model. Then, we focus on estimating the number of sources in a mixture, which is important for real-world scenarios. Our work on count estimation was motivated by a study on how humans can address this task, which lead us to conduct listening experiments, confirming that humans are only able to estimate the number of up to four sources correctly. To answer the question of whether machines can perform similarly, we present a DNN architecture, trained to estimate the number of concurrent speakers. Our results show improvements compared to other methods, and the model even outperformed humans on the same task. In both the source separation and source count estimation tasks, the key contribution of this thesis is the concept of “modulation”, which is important to computationally mimic human performance. Our proposed Common Fate Transform is an adequate representation to disentangle overlapping signals for separation, and an inspection of our DNN count estimation model revealed that it proceeds to find modulation-like intermediate features.Im Alltag sind wir von gemischten Signalen umgeben: Musik besteht aus einer Mischung von Instrumenten; in einem Meeting oder auf einer Konferenz sind wir einer Mischung menschlicher Stimmen ausgesetzt. Für diese Mischungen ist die automatische Quellentrennung oder die Bestimmung der Anzahl an Quellen eine anspruchsvolle Aufgabe. Eine häufige Annahme bei der Verarbeitung von gemischten Signalen im Zeit-Frequenzbereich ist, dass die Quellen sich nicht vollständig überlappen. In dieser Arbeit betrachten wir jedoch einige Fälle, in denen die Überlappung immens ist zum Beispiel, wenn Instrumente den gleichen Ton spielen (unisono) oder wenn viele Menschen gleichzeitig sprechen (Cocktailparty) —, so dass neue Signal-Repräsentationen und leistungsfähigere Modelle notwendig sind. Um die zwei genannten Probleme zu bewältigen, verwenden wir sowohl konventionelle Signalverbeitungsmethoden als auch tiefgehende neuronale Netze (DNN). Wir gehen zunächst auf das Problem der Quellentrennung für Unisono-Instrumentenmischungen ein und untersuchen die speziellen, durch Vibrato ausgelösten, zeitlich-spektralen Modulationen. Um diese Modulationen auszunutzen entwickelten wir eine Methode, die auf Zeitverzerrung basiert und eine Schätzung der Grundfrequenz als zusätzliche Information nutzt. Für Fälle, in denen diese Schätzungen nicht verfügbar sind, stellen wir ein unüberwachtes Modell vor, das inspiriert ist von der Art und Weise, wie Menschen zeitveränderliche Quellen gruppieren (Common Fate). Dieser Beitrag enthält eine neuartige Repräsentation, die die Separierbarkeit für überlappte und modulierte Quellen in Unisono-Mischungen erhöht, aber auch die Trennung in Gesang und Begleitung verbessert, wenn sie in einem DNN-Modell verwendet wird. Im Weiteren beschäftigen wir uns mit der Schätzung der Anzahl von Quellen in einer Mischung, was für reale Szenarien wichtig ist. Unsere Arbeit an der Schätzung der Anzahl war motiviert durch eine Studie, die zeigt, wie wir Menschen diese Aufgabe angehen. Dies hat uns dazu veranlasst, eigene Hörexperimente durchzuführen, die bestätigten, dass Menschen nur in der Lage sind, die Anzahl von bis zu vier Quellen korrekt abzuschätzen. Um nun die Frage zu beantworten, ob Maschinen dies ähnlich gut können, stellen wir eine DNN-Architektur vor, die erlernt hat, die Anzahl der gleichzeitig sprechenden Sprecher zu ermitteln. Die Ergebnisse zeigen Verbesserungen im Vergleich zu anderen Methoden, aber vor allem auch im Vergleich zu menschlichen Hörern. Sowohl bei der Quellentrennung als auch bei der Schätzung der Anzahl an Quellen ist ein Kernbeitrag dieser Arbeit das Konzept der “Modulation”, welches wichtig ist, um die Strategien von Menschen mittels Computern nachzuahmen. Unsere vorgeschlagene Common Fate Transformation ist eine adäquate Darstellung, um die Überlappung von Signalen für die Trennung zugänglich zu machen und eine Inspektion unseres DNN-Zählmodells ergab schließlich, dass sich auch hier modulationsähnliche Merkmale finden lassen

4G/5G cellular networks metrology and management

La prolifération d'applications et de services sophistiqués s'accompagne de diverses exigences de performances, ainsi que d'une croissance exponentielle du trafic pour le lien montant (uplink) et descendant (downlink). Les réseaux cellulaires tels que 4G et 5G évoluent pour prendre en charge cette quantité diversifiée et énorme de données. Le travail de cette thèse vise le renforcement de techniques avancées de gestion et supervision des réseaux cellulaires prenant l'explosion du trafic et sa diversité comme deux des principaux défis dans ces réseaux. La première contribution aborde l'intégration de l'intelligence dans les réseaux cellulaires via l'estimation du débit instantané sur le lien montant pour de petites granularités temporelles. Un banc d'essai 4G temps réel est déployé dans ce but de fournir un benchmark exhaustif des métriques de l'eNB. Des estimations précises sont ainsi obtenues. La deuxième contribution renforce le découpage 5G en temps réel au niveau des ressources radio dans un système multicellulaire. Pour cela, deux modèles d'optimisation ont été proposés. Du fait de leurs temps d'exécution trop long, des heuristiques ont été développées et évaluées en comparaisons des modèles optimaux. Les résultats sont prometteurs, les deux heuristiques renforçant fortement le découpage du RAN en temps réel.The proliferation of sophisticated applications and services comes with diverse performance requirements as well as an exponential traffic growth for both upload and download. The cellular networks such as 4G and 5G are advocated to support this diverse and huge amount of data. This thesis work targets the enforcement of advanced cellular network supervision and management techniques taking the traffic explosion and diversity as two main challenges in these networks. The first contribution tackles the intelligence integration in cellular networks through the estimation of users uplink instantaneous throughput at small time granularities. A real time 4G testbed is deployed for such aim with an exhaustive metrics benchmark. Accurate estimations are achieved.The second contribution enforces the real time 5G slicing from radio resources perspective in a multi-cell system. For that, two exact optimization models are proposed. Due to their high convergence time, heuristics are developed and evaluated with the optimal models. Results are promising, as two heuristics are highly enforcing the real time RAN slicing