Multiple source localization using spherical microphone arrays

Direction-of-Arrival (DOA) estimation is a fundamental task in acoustic signal processing and is used in source separation, localization, tracking, environment mapping, speech enhancement and dereverberation. In applications such as hearing aids, robot audition, teleconferencing and meeting diarization, the presence of multiple simultaneously active sources often occurs. Therefore DOA estimation which is robust to Multi-Source (MS) scenarios is of particular importance. In the past decade, interest in Spherical Microphone Arrays (SMAs) has been rapidly grown due to its ability to analyse the sound field with equal resolution in all directions. Such symmetry makes SMAs suitable for applications in robot audition where potential variety of heights and positions of the talkers are expected. Acoustic signal processing for SMAs is often formulated in the Spherical Harmonic Domain (SHD) which describes the sound field in a form that is independent of the geometry of the SMA. DOA estimation methods for the real-world scenarios address one or more performance degrading factors such as noise, reverberation, multi-source activity or tackled problems such as source counting or reducing computational complexity. This thesis addresses various problems in MS DOA estimation for speech sources each of which focuses on one or more performance degrading factor(s). Firstly a narrowband DOA estimator is proposed utilizing high order spatial information in two computationally efficient ways. Secondly, an autonomous source counting technique is proposed which uses density-based clustering in an evolutionary framework. Thirdly, a confidence metric for validity of Single Source (SS) assumption in a Time-Frequency (TF) bin is proposed. It is based on MS assumption in a short time interval where the number and the TF bin of active sources are adaptively estimated. Finally two analytical narrowband MS DOA estimators are proposed based on MS assumption in a TF bin. The proposed methods are evaluated using simulations and real recordings. Each proposed technique outperforms comparative baseline methods and performs at least as accurately as the state-of-the-art.Open Acces

Exposing and fixing causes of inconsistency and nondeterminism in clustering implementations

Cluster analysis aka Clustering is used in myriad applications, including high-stakes domains, by millions of users. Clustering users should be able to assume that clustering implementations are correct, reliable, and for a given algorithm, interchangeable. Based on observations in a wide-range of real-world clustering implementations, this dissertation challenges the aforementioned assumptions. This dissertation introduces an approach named SmokeOut that uses differential clustering to show that clustering implementations suffer from nondeterminism and inconsistency: on a given input dataset and using a given clustering algorithm, clustering outcomes and accuracy vary widely between (1) successive runs of the same toolkit, i.e., nondeterminism, and (2) different toolkits, i.e, inconsistency. Using a statistical approach, this dissertation quantifies and exposes statistically significant differences across runs and toolkits. This dissertation exposes the diverse root causes of nondeterminism or inconsistency, such as default parameter settings, noise insertion, distance metrics, termination criteria. Based on these findings, this dissertation introduces an automatic approach for locating the root causes of nondeterminism and inconsistency. This dissertation makes several contributions: (1) quantifying clustering outcomes across different algorithms, toolkits, and multiple runs; (2) using a statistical rigorous approach for testing clustering implementations; (3) exposing root causes of nondeterminism and inconsistency; and (4) automatically finding nondeterminism and inconsistency’s root causes

DENCAST: distributed density-based clustering for multi-target regression

Recent developments in sensor networks and mobile computing led to a huge increase in data generated that need to be processed and analyzed efficiently. In this context, many distributed data mining algorithms have recently been proposed. Following this line of research, we propose the DENCAST system, a novel distributed algorithm implemented in Apache Spark, which performs density-based clustering and exploits the identified clusters to solve both single- and multi-target regression tasks (and thus, solves complex tasks such as time series prediction). Contrary to existing distributed methods, DENCAST does not require a final merging step (usually performed on a single machine) and is able to handle large-scale, high-dimensional data by taking advantage of locality sensitive hashing. Experiments show that DENCAST performs clustering more efficiently than a state-of-the-art distributed clustering algorithm, especially when the number of objects increases significantly. The quality of the extracted clusters is confirmed by the predictive capabilities of DENCAST on several datasets: It is able to significantly outperform (p-value $$<0.05$$ ) state-of-the-art distributed regression methods, in both single and multi-target settings

ACTIVITY ANALYSIS OF SPECTATOR PERFORMER VIDEOS USING MOTION TRAJECTORIES

Spectator Performer Space (SPS) is a frequently occurring crowd dynamics, composed of one or more central performers, and a peripheral crowd of spectators. Analysis of videos in this space is often complicated due to occlusion and high density of people. Although there are many video analysis approaches, they are targeted for individual actors or low-density crowd and hence are not suitable for SPS videos. In this work, we present two trajectory-based features: Histogram of Trajectories (HoT) and Histogram of Trajectory Clusters (HoTC) to analyze SPS videos. HoT is calculated from the distribution of length and orientation of motion trajectories in a video. For HoTC, we compute the features derived from the motion trajectory clusters in the videos. So, HoTC characterizes different spatial region which may contain different action categories, inside a video. We have extended DBSCAN, a well-known clustering algorithm, to cluster short trajectories, common in SPS videos. The derived features are then used to classify the SPS videos based on their activities. In addition to using NaïveBayes and support vector machines (SVM), we have experimented with ensemble based classifiers and a deep learning approach using the videos directly for training. The efficacy of our algorithms is demonstrated using a dataset consisting of 4000 real life videos each from spectator and performer spaces. The classification accuracies for spectator videos (HoT: 87%; HoTC: 92%) and performer videos (HoT: 91%; HoTC: 90%) show that our approach out-performs t­­he state of the art techniques based on deep learning. Advisor: Ashok Sama

Tracking the Impact of the Covid-19 Pandemic with the Use of High-Frequency Geo-Located Bank Transaction Data

Using geo-located transaction data from 2 million customers of ABN AMRO bank in the Netherlands, this paper distinguishes the economic effects of consumers responses to the Covid-19 pandemic from those attributable to non-pharmaceutical interventions (NPIs). We compare municipalities that experienced large Covid-19 outbreaks with municipalities that had few or no cases and find that during the first Covid-19 wave the scale of the outbreak in a municipality has a strong negative effect on physical transactions by consumers in that municipality. This behavioral response function of consumers towards the virus is however not constant over time. During the second Covid-19 wave, the behavioural effect of consumers towards the virus has no real impact on consumption