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In the transition from traditional to digital musicology, large scale music data are increasingly becoming available which require research methods that work on the collection level and at scale. In the Digital Music Lab (DML) project, a software system has been developed that provides large-scale analysis of music audio with an interactive interface. The DML system includes distributed processing of audio and other music data, remote analysis of copyright-restricted data, logical inference on the extracted information and metadata, and visual web-based interfaces for exploring and querying music collections. A system prototype has been set up in collaboration with the British Library and I Like Music Ltd, which has been used to analyse a diverse corpus of over 250,000 music recordings. In this paper we describe the system requirements, architecture, components, and data sources, explaining their interaction. Use cases and applications with initial evaluations of the proposed system are also reported.

We describe an information-theoretic approach to the analysis of music and other sequential data, which emphasises the predictive aspects of perception, and the dynamic process of forming and modifying expectations about an unfolding stream of data, characterising these using the tools of information theory: entropies, mutual informations, and related quantities. After reviewing the theoretical foundations, we discuss a few emerging areas of application, including musicological analysis, real-time beat-tracking analysis, and the generation of musical materials as a cognitively-informed compositional aid. © 2012 IEEE.

We investigate the use of the Riemannian optimization method over the flag manifold in subspace ICA problems such as independent subspace analysis (ISA) and complex ICA. In the ISA experiment, we use the Riemannian approach over the flag manifold together with an MCMC method to overcome the problem of local minima of the ISA cost function. Experiments demonstrate the effectiveness of both Riemannian methods - simple geodesic gradient descent and hybrid geodesic gradient descent, compared with the ordinary gradient method.

SLAM in an outdoor environment using natural landmarks stands as the holy grail of SLAM algorithms. Segmenting landmarks from background clutter in such environments is difficult and vision, rather than laser, has a higher potential to perform such tasks due to the higher bandwidth of information it carries. There is a need to establish a benchmark upon which emerging vision SLAM algorithms can be assessed and compared. Towards this objective, this paper proposes the infrastructure for such a benchmark and discusses the issues involved in compiling it. Ego-motion information is extracted via a strap-down inertial measurement unit (IMU). Synchronized Global Positioning System (GPS), IMU, and surrounding images of an outdoor park environment are compiled into a database. IMU data in tested on an inertial navigation system (INS) dead-reckoning algorithm. The adequacy of the stereo image database is validated by extracting disparity maps of each stereo image in the database. IMU simulations show the necessity for visual SLAM to improve pose estimation. The complete data set, including GPS, IMU, and stereo images is available for downloading purposes

In this paper, we propose an algorithm that detects and locates natural objects in an outdoor environment using local descriptors. Interest points inside images are detected with a difference of Gaussian (DoG) filter and are then represented using scale invariant local descriptors. Our algorithm learns objects in a weakly supervised manner by clustering similar descriptors together and using those clusters as object classifiers. The intent is to identify stable objects to be used as landmarks for simultaneous localization and mapping (SLAM) of robots. The robot milieu is first identified using a fast environment recognition algorithm and then landmarks are suggested for SLAM that are appropriate for that environment. In our experiments we test our theory on the detection of trees that belong to the plantae pinophyta (pine family). Initial results show that out of 200 test images, our classification yields 85 correct positives, 15 false negatives, 73 correct negatives and 27 false positives.

The increase in hardware performance versus component cost has brought vision firmly into the realm of practical robotic sensors. We present an overview of an integrated research program to create a general-purpose robotic vision system from an expansive rather than linear perspective, incorporating a multimodal approach to real-time visual interaction with the environment. Design and construction of a high-performance active camera platform using primarily off-the-shelf components has proceeded in parallel with algorithm design and implementation towards a suite of low-level behaviours suitable for feeding quantised data to a multitude of visually guided tasks, from mobile robot navigation and visual servoing to human-robot interaction and instruction. We describe these initiatives, including performance specifications and experimental results obtained under real-world conditions.

We consider a visually-guided autonomous underwater vehicle. We develop a position-based visual servo control of fixed and slow moving targets using visual position feedback and sensor-based orientation feedback. The visual position feedback is implemented on a stereo camera system. We use a compass and an inclinometer for orientation feedback. We also implement a computed torque controller, using Euler parameters to represent the orientation state, for the vehicle motion control. Using Euler parameters eliminates singularities in the model and the controller. Preliminary experimental results of visual servo control are reported.

Develops a model that identifies changes in process control parameter values by analysing the geometry of a product from a forging process. That is, shape variations from the norm of a forged part are linked to the variations of the process parameters of the forging process. This is an important aspect of inspection analysis because it implies that the analysis of shape variation can return the potential errors within the product and also the changes needed to the process control parameters to correct errors in the product shape. Our model can identify whether input parameters of interest are too high or too low to an accuracy of at least 70%. Given the complexity of the forging process these initial results show promise.