Data-driven Threshold Selection for Direct Path Dominance Test

Direction-of-arrival estimation methods, when used with recordings made in enclosures are negatively affected by the reflections and reverberation in that enclosure. Direct path dominance (DPD) test was proposed as a pre-processing stage which can provide better DOA estimates by selecting only the time-frequency bins with a single dominant sound source component prior to DOA estimation, thereby reducing the total computational cost. DPD test involves selecting bins for which the ratio of the two largest singular values of the local spatial correlation matrix is above a threshold. The selection of this threshold is typically carried out in an ad hoc manner, which hinders the generalisation of this approach. This selection method also potentially increases the total computational cost or reduces the accuracy of DOA estimation. We propose a DPD test threshold selection method based on a data-driven statistical model. The model is based on the approximation of the singular value ratio distribution of the spatial correlation matrices as a generalised Pareto distribution and allows selecting time-frequency bins based on their probability of occurrence. We demonstrate the application of this threshold selection method via emulations using acoustic impulse responses measured in a highly reverberant room with a rigid spherical microphone array

Mikrofon dizinleri için entropi temelli varış yönü kestirme yöntemleri.

Direction-of-arrival (DOA) estimation of sound sources is a popular topic of research and has an important role in several different applications including spatial audio. Recent advances in microphone arrays made more accurate sound field analysis possible. Spherical microphone arrays afford a trivial calculation of spherical harmonic decomposition of sound fields and can be employed in different DOA estimation methods in spherical harmonics domain. This thesis proposes a novel DOA estimation method called Hierarchical Grid Refinement (HiGRID) for rigid spherical microphone arrays (RSMA). This method is based on the calculation of the sector averaged directional response power of a steered beam over a sparse set of directions on the unit sphere. The selection of the direction for which response power is to be calculated is determined using spatial entropy as a criterion. A new clustering method based on connected components labelling is also proposed for counting sources and estimating their DOAs. In addition to HiGRID, this work investigates several state-of-the-art DOA estimation techniques. These include the improvement of DOA estimation performance or computational efficiency of Eigenbeam Multiple Signal Classification (EB-MUSIC) and Direct Path Dominance (DPD) test. HiGRID is first used as source counting method prior to EB-MUSIC to decrease the computational cost of DOA estimation. HiGRID is then used as a DOA estimation method following the DPD test which increases the DOA estimation accuracy while reducing the total computational cost. A new data-driven statistical method for DPD test threshold selection is also proposed. This allows the an informed selection of DPD test threshold based on effective rank statistics of spatial correlation matrices obtained from RSMAs. Comparison of HiGRID with previous DOA estimation methods with real and simulated recordings are presented. Evaluations of proposed algorithms for EB*MUSIC and DPD test are also presented in terms of DOA estimation errors using simulated recordings. HiGRID and its combinations with EB-MUSIC and DPD test performed favourably in comparison with other state-of-the-art DOA estimation methods indicating the utility of the proposed methods in DOA estimation.Thesis (M.S.) -- Graduate School of Informatics. Modeling and Simulation

LOCALIZATION OF MULTIPLE SOURCES IN THE SPHERICAL HARMONIC DOMAIN WITH HIERARCHICAL GRID REFINEMENT AND EB-MUSIC

Direction-of-arrival (DOA) estimation is an important step in acoustic scene analysis. Multiple signal classification in the eigenbeam domain (EB-MUSIC) is an accurate direction-of-arrival (DOA) estimation method for rigid spherical microphone arrays. Two important issues with this method are 1) the requirement of prior information about the number of coherent source components, and 2) its computational cost. In this paper, a computationally efficient two-stage method, which can alleviate these problems, is proposed. The proposed method is robust to reverberation, and can be used to efficiently localize multiple coherent sources. An evaluation of the method using simulated recordings under highly reverberant conditions is also presented

Rotation Calibration of Rigid Spherical Microphone Arrays for Multi-perspective 6DoF Audio Recordings

The preferred approach for multi-perspective six-degrees-of-freedom (6DoF) audio involves using multiple rigid spherical microphone arrays (RSMA) that can capture higher-order Ambisonics. RSMAs are spherically symmetric and allow the calculation of the local decomposition of sound fields over spherical harmonic functions. When multiple such arrays are used, multiple scattering occurs that can be equalized via methods that rely on multipole expansions if the positions of the arrays are known and the coordinate frames of arrays are aligned. In a practical scenario, however, when such a set of arrays are placed in location, their positions will not be exact and their coordinate frames will not be fully aligned. This paper is concerned with the correction of rotational mismatches in azimuth angles of individual RSMAs. The effects of such misalignment on the calculation of the local multipole expansion of the sound field are numerically explored. A rotation calibration approach that depends on non-linear optimisation is proposed. Numerical evaluations of different scenarios are presented

Road and railway detection in SAR images using deep learning

Detection and segmentation of motorways, railroads and other roads with similar features are significant for comprehension of both low and high resolution synthetic aperture radar (SAR) imagery. Separation of transportation network from other fields or features is important to understand area contained in SAR image (i.e. the road density can inform about characteristic of that area). Standard image processing methods are inadequate to detect multiple linear targets correctly where computer vision, especially deep learning, provides more insight about features for different type of roads which help better discrimination of multiple linear features like roads and railroads. State-of-art deep learning algorithms are proposed as solutions for understanding road characteristics and extraction of multiple roads. In this paper, a method which uses deep convolutional neural network (DeepLabv3+) backbone architecture is proposed to detect road and railways concurrently. Semantic segmentation of roads using SAR imagery is challenging since these images differ as ground sample distance changes with sensor types which creates a setback for establishing dataset for all sensors. Training set contains 3 classes (road, railway, other) with collected signatures from TerraSAR-X Spotlight images for classification. Proposed method shows robust performance when applied to other sensor and results are presented

Multiple Sound Source Localization With Steered Response Power Density and Hierarchical Grid Refinement

Estimation of the direction-of-arrival (DOA) of sound sources is an important step in sound field analysis. Rigid spherical microphone arrays allow the calculation of a compact spherical harmonic representation of the sound field. The standard method for analyzing sound fields recorded using such arrays is steered response power (SRP) maps wherein the source DOA can be estimated as the steering direction that maximizes the output power of a maximally directive beam. This approach is computationally costly since it requires steering the beam in all possible directions. This paper presents an extension to SRP called steered response power density (SRPD) and an associated, signal-adaptive search method called hierarchical grid refinement for reducing the number of steering directions needed for DOA estimation. The proposed method can localize near-coherent as well as incoherent sources while jointly providing the number of prominent sources in the scene. It is shown to be robust to reverberation and additive white noise. An evaluation of the proposed method using simulations and real recordings under highly reverberant conditions as well as a comparison with the state-of-the-art methods are presented

Bulutsuzluk Özlemi'nin öncülük ettiği, Türkiye'de rock müziği'nin 70'li ve 80'li yıllardaki tarihsel gelişimi

Ankara : İhsan Doğramacı Bilkent Üniversitesi İktisadi, İdari ve Sosyal Bilimler Fakültesi, Tarih Bölümü, 2014.This work is a student project of the The Department of History, Faculty of Economics, Administrative and Social Sciences, İhsan Doğramacı Bilkent University.by Yeni, Harun