Audio Source Positioning Based on Angle of Arrival Measurements

Estimating position is done in various contexts from locating phones with GPS to locating boats using hydrophones. In this thesis we study estimating audio source position based on angle of arrival measurements. Multiple different filters can be used on measured angles of arrival to deduce the position of the source. The filter to be used in this work was chosen to be the particle filter. Even though particle filter is computationally more heavy than many other filters, modern computers can simulate hundreds of particles in a short time without too much of an effort. We introduce the reader to the use of particle filter in positioning, along with theoretical background of it and positioning in a more general sense. The data in this work is recorded in either an anechoic chamber or a room that has no special equipment installed to enhance audio quality in it. The measurements are done with a mobile device with four microphones. Audio source in the anechoic chamber is a loudspeaker playing speech or a person speaking and walking randomly in the room. If the data contains noise, it is played from loudspeakers in the same space as the source is located in. Another type of data handled in this work is measured outside in a racing event where multiple cars passed the measurement device as well as generated data with multiple sources. The data is handled as a mixture between von Mises and uniform distribution. An important parameter of von Mises distribution is a variable called κ, which tells the concentration of the distribution. In this work we show and prove a way to estimate said variable with maximum likelihood method. Additionally, we introduce the reader to mathematical background of particle filter and positioning in more general sense. Results given by the particle filter depend on the chosen value of κ along with chosen q-value, which tells the smoothness of the result, and measurement model. Finally, we present and compare the results obtained by constant velocity and random walk models with several different q-values

3D angle-of-arrival positioning using von Mises-Fisher distribution

We propose modeling an angle-of-arrival (AOA) positioning measurement as a von Mises-Fisher (VMF) distributed unit vector instead of the conventional normally distributed azimuth and elevation measurements. Describing the 2-dimensional AOA measurement with three numbers removes discontinuities and reduces nonlinearity at the poles of the azimuth-elevation coordinate system. Our computer simulations show that the proposed VMF measurement noise model based filters outperform the normal distribution based algorithms in accuracy in a scenario where close-to-pole measurements occur frequently.Comment: 5 page

Multichannel source separation and tracking with phase differences by random sample consensus

Blind audio source separation (BASS) is a fascinating problem that has been tackled from many different angles. The use case of interest in this thesis is that of multiple moving and simultaneously-active speakers in a reverberant room. This is a common situation, for example, in social gatherings. We human beings have the remarkable ability to focus attention on a particular speaker while effectively ignoring the rest. This is referred to as the ``cocktail party effect'' and has been the holy grail of source separation for many decades. Replicating this feat in real-time with a machine is the goal of BASS. Single-channel methods attempt to identify the individual speakers from a single recording. However, with the advent of hand-held consumer electronics, techniques based on microphone array processing are becoming increasingly popular. Multichannel methods record a sound field from various locations to incorporate spatial information. If the speakers move over time, we need an algorithm capable of tracking their positions in the room. For compact arrays with 1-10 cm of separation between the microphones, this can be accomplished by applying a temporal filter on estimates of the directions-of-arrival (DOA) of the speakers. In this thesis, we review recent work on BSS with inter-channel phase difference (IPD) features and provide extensions to the case of moving speakers. It is shown that IPD features compose a noisy circular-linear dataset. This data is clustered with the RANdom SAmple Consensus (RANSAC) algorithm in the presence of strong reverberation to simultaneously localize and separate speakers. The remarkable performance of RANSAC is due to its natural tendency to reject outliers. To handle the case of non-stationary speakers, a factorial wrapped Kalman filter (FWKF) and a factorial von Mises-Fisher particle filter (FvMFPF) are proposed that track source DOAs directly on the unit circle and unit sphere, respectively. These algorithms combine directional statistics, Bayesian filtering theory, and probabilistic data association techniques to track the speakers with mixtures of directional distributions

Sound-source position tracking from direction-of-arrival measurements: Application to distributed first-order spherical microphone arrays

Rendering 6-degrees-of-freedom (6DoF) spatial audio requires sound-source position tracking. Without further assumptions, directional receivers, such as a spherical microphone array (SMA), can estimate the direction of arrival (DoA), but not reliably estimate sound-source distance. By utilizing multiple, distributed SMAs, further methods are available that directly infer the position in 3-D space. Typically used DoA intersection by triangulation delivers problematically noisy estimates, therefore, statistical filters are better suited. In this study, we compare the performance of different DoA to position tracking strategies. DoA angles suffer from the well-known angle wrapping problem, which is especially problematic in Gaussian filters. However, these filters are attractive due to their low computational complexity. Using circular and spherical statistics, the non- linear extensions of the Kalman filter can be formulated to explicitly treat the discontinuity of DoA angles. Furthermore, we introduce a time adaptive regularization of the filter update by the instantaneous sound-field diffuseness estimate. An experiment with three first-order SMAs in a reverberant room shows an improved distance error compared to the mean DoA intersection baseline. The results highlight the importance of treating the angle wrapping and the stabilization when incorporating the sound-field diffuseness estimate.publishedVersionNon peer reviewe

Multitarget Tracking Using Orientation Estimation for Optical Belt Sorting

In optical belt sorting, accurate predictions of the bulk material particles’ motions are required for high-quality results. By implementing a multitarget tracker tailored to the scenario and deriving novel motion models, the predictions are greatly enhanced. The tracker’s reliability is improved by also considering the particles’ orientations. To this end, new estimators for directional quantities based on orthogonal basis functions are presented and shown to outperform the state of the art

Directional Estimation for Robotic Beating Heart Surgery

In robotic beating heart surgery, a remote-controlled robot can be used to carry out the operation while automatically canceling out the heart motion. The surgeon controlling the robot is shown a stabilized view of the heart. First, we consider the use of directional statistics for estimation of the phase of the heartbeat. Second, we deal with reconstruction of a moving and deformable surface. Third, we address the question of obtaining a stabilized image of the heart