Self-Localization of Ad-Hoc Arrays Using Time Difference of Arrivals

This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/K007491/1

Affine Projection Algorithm Over Acoustic Sensor Networks for Active Noise Control

[EN] Acoustic sensor networks (ASNs) are an effective solution to implement active noise control (ANC) systems by using distributed adaptive algorithms. On one hand, ASNs provide scalable systems where the signal processing load is distributed among the network nodes. On the other hand, their noise reduction performance is comparable to that of their respective centralized processing systems. In this sense, the distributed multiple error filtered-x least mean squares (DMEFxLMS) adaptive algorithm has shown to obtain the same performance than its centralized counterpart as long as there are no communications constraints in the underlying ASN. Regarding affine projection (AP) adaptive algorithms, some distributed approaches that are approximated versions of the multichannel filtered-x affine projection (MFxAP) algorithm have been previously proposed. These AP algorithms can efficiently share the processing load among the nodes, but at the expense of worsening their convergence properties. In this paper we develop the exact distributed multichannel filtered-x AP (EFxAP) algorithm, which obtains the same solution as that of the MFxAP algorithm as long as there are no communications constraints in the underlying ASN. In the EFxAP algorithm each node can compute a part or the entire inverse matrix needed by the centralized MFxAP algorithm. Thus, we propose three different strategies that obtain significant computational saving: 1) Gauss Elimination, 2) block LU factorization, and 3) matrix inversion lemma. As a result, each node computes only between 25%¿60% of the number of multiplications required by the direct inversion of the matrix. Regarding the performance in transient and steady states, the EFxAP exhibits the fastest convergence and the highest noise level reduction for any size of the acoustic network and any projection order of the AP algorithm compared to the DMEFxLMS and two previously reported distributed AP algorithms.This work was supported by EU together with Spanish Government through RTI2018-098085B-C41 (MINECO/FEDER) and Generalitat Valenciana through PROMETEO/2019/109.Ferrer Contreras, M.; Diego Antón, MD.; Piñero, G.; Gonzalez, A. (2021). Affine Projection Algorithm Over Acoustic Sensor Networks for Active Noise Control. IEEE/ACM Transactions on Audio Speech and Language Processing. 29:448-461. https://doi.org/10.1109/TASLP.2020.3042590S4484612

Simultaneous ranging and self-positioning in unsynchronized wireless acoustic sensor networks

Automatic ranging and self-positioning is a very desirable property in wireless acoustic sensor networks (WASNs) where nodes have at least one microphone and one loudspeaker. However, due to environmental noise, interference and multipath effects, audio-based ranging is a challenging task. This paper presents a fast ranging and positioning strategy that makes use of the correlation properties of pseudo-noise (PN) sequences for estimating simultaneously relative time-of-arrivals (TOAs) from multiple acoustic nodes. To this end, a proper test signal design adapted to the acoustic node transducers is proposed. In addition, a novel self-interference reduction method and a peak matching algorithm are introduced, allowing for increased accuracy in indoor environments. Synchronization issues are removed by following a BeepBeep strategy, providing range estimates that are converted to absolute node positions by means of multidimensional scaling (MDS). The proposed approach is evaluated both with simulated and real experiments under different acoustical conditions. The results using a real network of smartphones and laptops confirm the validity of the proposed approach, reaching an average ranging accuracy below 1 centimeter.This work was supported by the Spanish Ministry of Economy and Competitiveness under Grant TIN2015-70202-P, TEC2012-37945-C02-02 and FEDER funds

A Survey of Sound Source Localization Methods in Wireless Acoustic Sensor Networks

Wireless acoustic sensor networks (WASNs) are formed by a distributed group of acoustic-sensing devices featuring audio playing and recording capabilities. Current mobile computing platforms offer great possibilities for the design of audio-related applications involving acoustic-sensing nodes. In this context, acoustic source localization is one of the application domains that have attracted the most attention of the research community along the last decades. In general terms, the localization of acoustic sources can be achieved by studying energy and temporal and/or directional features from the incoming sound at different microphones and using a suitable model that relates those features with the spatial location of the source (or sources) of interest. This paper reviews common approaches for source localization in WASNs that are focused on different types of acoustic features, namely, the energy of the incoming signals, their time of arrival (TOA) or time difference of arrival (TDOA), the direction of arrival (DOA), and the steered response power (SRP) resulting from combining multiple microphone signals. Additionally, we discuss methods not only aimed at localizing acoustic sources but also designed to locate the nodes themselves in the network. Finally, we discuss current challenges and frontiers in this field

잔향 환경에서의 인공 음향 신호를 이용한 음향 센서 위치 추정 기술

학위논문 (박사)-- 서울대학교 대학원 공과대학 전기·컴퓨터공학부, 2017. 8. 김남수.Widespread use of smart devices has brought a growth of user-customized services. In particular, localization techniques have been gaining attention due to increase of location-based services (LBS). Most of LBS services such as navigation systems, traffic alerts or augmented reality (AR) services depend on the GPS for its accuracy and speed, however, its operation is limited to the outdoor environments. The demand of indoor LBS is rapidly growing due to the growth of automated home and IoT technology. There have been studies via WiFi, Bluetooth or RFID, but their performance has been unsatisfactory for their limitation such as the requirement of additional equipment or guarantee of the line of sight. Among various sensors used for indoor localization, we focus on the acoustic sensors, i.e. microphones. There are several advantages in using the acoustic signals for indoor localization. There is no need for additional apparatus since loudspeakers are pre-installed in most of the buildings for the purpose of announcement or playing background music and mobile devices such as cellphones or tablets are equipped with microphones and loudspeakers. Even the prevailing popularity of IoT services helps accessibility of acoustical sensors and loudspeakers. In addition, acoustic signals have advantages of being able to detect signals through obstacles unlike cameras of RFID. In this thesis, we propose a position estimation system using acoustic signals to maximize these advantages. We aim to estimate the position of the target user with an acoustic sensor based on the recording of signals from the fixed loudspeakers installed around the room. We target to estimate the position of the acoustic sensor with high accuracy and low-complexity in a large space with high reverberation. Particularly, we try not to affect human hearing by using inaudible frequency bands. In order to estimate the position, it is important to estimate the direct path signal rather than the signal due to reverberation or reflection. To do this, we present various localization techniques as following. First, we propose the source data structure to operate in the large reverberant environments. In the large space, the consideration of the near-far effect is required which refers to a situation when the desired signal is far away, it is difficult to receive the desired signal due to the interference of closer unwanted signals. In wireless communications, it can be dealt with by interaction of transmitter and receiver by feedback of channel information. However, it is difficult in the acoustic system since there is no feedback between the transmitter and receiver. We borrowed the structure called OFDMA-CDM and modified it to deal with the near-far effect. In the reverberant environment, the amplitude of reverberation is often larger than the direct path signal. We proposed the technique to estimate the direct path signal. Second, we propose a method for accurate location estimation in the highly reverberant environments. In the high reverberation condition, more spurious reflections occur, which makes it difficult to estimate the time delay of the direct path signal. If the time delay estimation is wrong, it is likely that the position estimate does not converge by an estimation method. In the proposed method, position candidates are obtained from most of the received signals including signals even from spurious reflections. The unreliable candidates are filtered out by the agreement test and rank the rest candidates by their reliability to find accurate target position. We can estimate the receiver's position even in the condition of attenuated direct path signal or high reverberation by using the proposed method. Third, we proposed a low-complexity localization method to work in the highly reverberant environment. This method is based on the particle filter that estimates the position by weighted particles whose weights are computed by the likelihood. We designed likelihood function that efficiently calculates likelihood in the region with the direct path signal so that more reliable position can be obtained. The proposed method enables location estimation with high precision with a relatively small amount of computation in severe reverberation. The proposed methods are evaluated in simulated environments with different reverberation time. The performances are verified in different parameters and compared with other localization methods. In addition, the performance is evaluated in the real reverberant environment with a large space. A series of experiments has shown the superiority of the proposed methods and it is appropriate to apply in the actual environment.1 Introduction 1 2 Acoustic Receiver Localization System 7 2.1 Source data structure 8 2.2 Localization from the received signal 12 2.3 TDE in reverberant environments 16 2.4 Near-far effect 18 3 Indoor Localization using Inaudible Acoustic Signals 21 3.1 Introduction 21 3.2 Acoustic source design and synchronization 22 3.2.1 Reverberation in multipath environments 23 3.2.2 Source data structure for ARL 23 3.2.3 Signal presence detection 30 3.2.4 Direct path detection 30 3.3 Performance evaluation 32 3.3.1 Experimental setup and system configuration 33 3.3.2 Evaluation of acoustic data structure 34 3.3.3 Performance of the direct path detection algorithm 36 3.3.4 Performance in a real room 36 3.4 Summary 38 4 Robust Time Delay Estimation for Acoustic Indoor Localization in Reverberant Environments 39 4.1 Introduction 39 4.2 Robust TDE 40 4.3 Performance evaluation 45 4.3.1 Performance evaluation in a real room 46 4.3.2 Performance evaluation in simulated reverberant conditions 47 4.4 Summary 50 5 Indoor Localization Based on Particle Filtering 53 5.1 Introduction 53 5.2 A framework of positioning method using particle filter 54 5.2.1 State and dynamic models 55 5.2.2 Bayesian framework using particle filter 56 5.2.3 Likelihood function 57 5.3 ARL in reverberant environment 59 5.3.1 Peak quality 59 5.3.2 Efficient calculation of the likelihood function 60 5.3.3 Finding the direct path region 61 5.4 Performance evaluation 64 5.4.1 Performance in a simulated environment 65 5.4.2 Performance in the actual environment 87 5.5 Summary 89 6 Conclusions 91 Bibliography 95 요약 105Docto