642 research outputs found
Adaptive Sampling with Mobile Sensor Networks
Mobile sensor networks have unique advantages compared with wireless sensor networks. The mobility enables mobile sensors to flexibly reconfigure themselves to meet sensing requirements. In this dissertation, an adaptive sampling method for mobile sensor networks is presented. Based on the consideration of sensing resource constraints, computing abilities, and onboard energy limitations, the adaptive sampling method follows a down sampling scheme, which could reduce the total number of measurements, and lower sampling cost. Compressive sensing is a recently developed down sampling method, using a small number of randomly distributed measurements for signal reconstruction. However, original signals cannot be reconstructed using condensed measurements, as addressed by Shannon Sampling Theory. Measurements have to be processed under a sparse domain, and convex optimization methods should be applied to reconstruct original signals. Restricted isometry property would guarantee signals can be recovered with little information loss. While compressive sensing could effectively lower sampling cost, signal reconstruction is still a great research challenge. Compressive sensing always collects random measurements, whose information amount cannot be determined in prior. If each measurement is optimized as the most informative measurement, the reconstruction performance can perform much better.
Based on the above consideration, this dissertation is focusing on an adaptive sampling approach, which could find the most informative measurements in unknown environments and reconstruct original signals. With mobile sensors, measurements are collect sequentially, giving the chance to uniquely optimize each of them. When mobile sensors are about to collect a new measurement from the surrounding environments, existing information is shared among networked sensors so that each sensor would have a global view of the entire environment. Shared information is analyzed under Haar Wavelet domain, under which most nature signals appear sparse, to infer a model of the environments. The most informative measurements can be determined by optimizing model parameters. As a result, all the measurements collected by the mobile sensor network are the most informative measurements given existing information, and a perfect reconstruction would be expected.
To present the adaptive sampling method, a series of research issues will be addressed, including measurement evaluation and collection, mobile network establishment, data fusion, sensor motion, signal reconstruction, etc. Two dimensional scalar field will be reconstructed using the method proposed. Both single mobile sensors and mobile sensor networks will be deployed in the environment, and reconstruction performance of both will be compared.In addition, a particular mobile sensor, a quadrotor UAV is developed, so that the adaptive sampling method can be used in three dimensional scenarios
Automatic transcription of polyphonic music exploiting temporal evolution
PhDAutomatic music transcription is the process of converting an audio recording
into a symbolic representation using musical notation. It has numerous applications
in music information retrieval, computational musicology, and the
creation of interactive systems. Even for expert musicians, transcribing polyphonic
pieces of music is not a trivial task, and while the problem of automatic
pitch estimation for monophonic signals is considered to be solved, the creation
of an automated system able to transcribe polyphonic music without setting
restrictions on the degree of polyphony and the instrument type still remains
open.
In this thesis, research on automatic transcription is performed by explicitly
incorporating information on the temporal evolution of sounds. First efforts address
the problem by focusing on signal processing techniques and by proposing
audio features utilising temporal characteristics. Techniques for note onset and
offset detection are also utilised for improving transcription performance. Subsequent
approaches propose transcription models based on shift-invariant probabilistic
latent component analysis (SI-PLCA), modeling the temporal evolution
of notes in a multiple-instrument case and supporting frequency modulations in
produced notes. Datasets and annotations for transcription research have also
been created during this work. Proposed systems have been privately as well as
publicly evaluated within the Music Information Retrieval Evaluation eXchange
(MIREX) framework. Proposed systems have been shown to outperform several
state-of-the-art transcription approaches.
Developed techniques have also been employed for other tasks related to music
technology, such as for key modulation detection, temperament estimation,
and automatic piano tutoring. Finally, proposed music transcription models
have also been utilized in a wider context, namely for modeling acoustic scenes
Robust Bayesian Pitch Tracking Based on the Harmonic Model
Fundamental frequency is one of the most important characteristics of speech and audio signals. Harmonic model-based fundamental frequency estimators offer a higher estimation accuracy and robustness against noise than the widely used autocorrelation-based methods. However, the traditional harmonic model-based estimators do not take the temporal smoothness of the fundamental frequency, the model order, and the voicing into account as they process each data segment independently. In this paper, a fully Bayesian fundamental frequency tracking algorithm based on the harmonic model and a first-order Markov process model is proposed. Smoothness priors are imposed on the fundamental frequencies, model orders, and voicing using first-order Markov process models. Using these Markov models, fundamental frequency estimation and voicing detection errors can be reduced. Using the harmonic model, the proposed fundamental frequency tracker has an improved robustness to noise. An analytical form of the likelihood function, which can be computed efficiently, is derived. Compared to the state-of-the-art neural network and nonparametric approaches, the proposed fundamental frequency tracking algorithm has superior performance in almost all investigated scenarios, especially in noisy conditions. For example, under 0 dB white Gaussian noise, the proposed algorithm reduces the mean absolute errors and gross errors by 15% and 20% on the Keele pitch database and 36% and 26% on sustained /a/ sounds from a database of Parkinson's disease voices. A MATLAB version of the proposed algorithm is made freely available for reproduction of the results. 1 1An implementation of the proposed algorithm using MATLAB may be found in https://tinyurl.com/yxn4a543
A Parametric Method for Multi-Pitch Estimation
This thesis proposes a novel method for multi-pitch estimation. The method operates by posing pitch estimation as a sparse recovery problem which is solved using convex optimization techniques. In that respect, it is an extension of an earlier presented estimation method based on the group-LASSO. However, by introducing an adaptive total variation penalty, the proposed method requires fewer user supplied parameters, thereby simplifying the estimation procedure. The method is shown to have comparable to superior performance in low noise environments when compared to three standard multi-pitch estimation methods as well as the predecessor method. Also presented is a scheme for automatic selection of the regularization parameters, thereby making the method more user friendly. Used together with this scheme, the proposed method is shown to yield accurate, although not statistically efficent, pitch Estimates when evaluated on synthetic speech data
Sparse Bayesian information filters for localization and mapping
Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2008This thesis formulates an estimation framework for Simultaneous Localization and
Mapping (SLAM) that addresses the problem of scalability in large environments.
We describe an estimation-theoretic algorithm that achieves significant gains in computational
efficiency while maintaining consistent estimates for the vehicle pose and
the map of the environment.
We specifically address the feature-based SLAM problem in which the robot represents
the environment as a collection of landmarks. The thesis takes a Bayesian
approach whereby we maintain a joint posterior over the vehicle pose and feature
states, conditioned upon measurement data. We model the distribution as Gaussian
and parametrize the posterior in the canonical form, in terms of the information
(inverse covariance) matrix. When sparse, this representation is amenable to computationally
efficient Bayesian SLAM filtering. However, while a large majority of the
elements within the normalized information matrix are very small in magnitude, it is
fully populated nonetheless. Recent feature-based SLAM filters achieve the scalability
benefits of a sparse parametrization by explicitly pruning these weak links in an effort
to enforce sparsity. We analyze one such algorithm, the Sparse Extended Information
Filter (SEIF), which has laid much of the groundwork concerning the computational
benefits of the sparse canonical form. The thesis performs a detailed analysis of the
process by which the SEIF approximates the sparsity of the information matrix and
reveals key insights into the consequences of different sparsification strategies. We
demonstrate that the SEIF yields a sparse approximation to the posterior that is inconsistent,
suffering from exaggerated confidence estimates. This overconfidence has
detrimental effects on important aspects of the SLAM process and affects the higher
level goal of producing accurate maps for subsequent localization and path planning.
This thesis proposes an alternative scalable filter that maintains sparsity while
preserving the consistency of the distribution. We leverage insights into the natural
structure of the feature-based canonical parametrization and derive a method that
actively maintains an exactly sparse posterior. Our algorithm exploits the structure
of the parametrization to achieve gains in efficiency, with a computational cost that
scales linearly with the size of the map. Unlike similar techniques that sacrifice
consistency for improved scalability, our algorithm performs inference over a posterior
that is conservative relative to the nominal Gaussian distribution. Consequently, we
preserve the consistency of the pose and map estimates and avoid the effects of an
overconfident posterior.
We demonstrate our filter alongside the SEIF and the standard EKF both in simulation
as well as on two real-world datasets. While we maintain the computational
advantages of an exactly sparse representation, the results show convincingly that
our method yields conservative estimates for the robot pose and map that are nearly
identical to those of the original Gaussian distribution as produced by the EKF, but
at much less computational expense.
The thesis concludes with an extension of our SLAM filter to a complex underwater
environment. We describe a systems-level framework for localization and mapping
relative to a ship hull with an Autonomous Underwater Vehicle (AUV) equipped
with a forward-looking sonar. The approach utilizes our filter to fuse measurements
of vehicle attitude and motion from onboard sensors with data from sonar images of
the hull. We employ the system to perform three-dimensional, 6-DOF SLAM on a
ship hull
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