Audio Tracking in Noisy Environments by Acoustic Map and Spectral Signature

Crocco, Marco; Martelli, Samuele; Trucco, A.; Zunino, Andrea; Murino, Vittorio

doi:10.1109/tcyb.2017.2711497

Cited by 18 publications

(10 citation statements)

References 36 publications

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“…Under given hypotheses on the target nature and on its motion constraints, it is possible to pair the localization signal-processing methods with tracking techniques based on motion dynamics modeling, which allow to forecast the target position to a certain extent. Recent approaches to acoustic tracking may also exploit the timbre characteristics of the source to recognize its acoustic signature and improve the tracking [18].…”

Section: Introductionmentioning

confidence: 99%

Acoustic Target Tracking Through a Cluster of Mobile Agents

Drioli

Giordano

Salvati

et al. 2021

IEEE Trans. Cybern.

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This paper discusses the problem of tracking a moving target by means of a cluster of mobile agents that is able to sense the acoustic emissions of the target, with the aim of improving the target localization and tracking performance with respect to conventional fixed-array acoustic localization. We handle the acoustic part of the problem by modeling the cluster as a sensor network, and we propose a centralized control strategy for the agents that exploits the spatial sensitivity pattern of the sensor network to estimate the best possible cluster configuration with respect to the expected target position. In order to take into account the position estimation delay due to the frame-based nature of the processing, the possible positions of the acoustic target in a given future time interval are represented in terms of a compatible set, that is, the set of all possible future positions of the target, given its dynamics and its present state. A frame-by-frame cluster reconfiguration algorithm is presented, which adapts the position of each sensing agent with the goal of pursuing the maximum overlap between the region of high acoustic sensitivity of the entire cluster and the compatible set of the sound-emitting target. The tracking scheme iterates, at each observation frame, the computation of the target compatible set, the reconfiguration of the cluster, and the target acoustic localization. The reconfiguration step makes use of an opportune cost function proportional to the difference of the compatibility set and the acoustic sensitivity spatial pattern determined by the mobile agent positions. Simulations under different geometric configurations and positioning constraints demonstrate the ability of the proposed approach to effectively localize and track a moving target based on its acoustic emission. The Doppler effect related to moving sources and sensors is taken into account, and its impact on performance is analyzed. We compare the localization results with conventional static-array localization and positioning of acoustic sensors through genetic algorithm optimization, and results demonstrate the sensible improvements in terms of localization and tracking performance. Although the method is discussed here with respect to acoustic target tracking, it can be effectively adapted to video-based localization and

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Section: Introductionmentioning

confidence: 99%

Acoustic Target Tracking Through a Cluster of Mobile Agents

Drioli

Giordano

Salvati

et al. 2021

IEEE Trans. Cybern.

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“…In fact, it is wavelength that determines the amount of radiation reflected, scattered, absorbed, or emitted by each material. As a result, spectral signature is highly valued in many real-world applications [3,4], including but not limited to classification [5,6], target detection [7,8], and spectral unmixing [9,10].…”

Section: Introductionmentioning

confidence: 99%

Classification of Remote Sensing Images Through Reweighted Sparse Subspace Representation Using Compressed Data

Zhu¹,

Zhao²,

Wu³

2021

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In many real-world scenarios, subspace clustering essentially aims to cluster unlabeled high-dimensional data into a union of finite-dimensional linear subspaces. The problem is that the data are always high-dimensional, with the increase of the computation, storge, and communication of various intelligent data-driven systems. This paper attempts to develop a method to cluster spectral images directly using the measurements of compressive coded aperture snapshot spectral imager (CASSI), eliminating the need to reconstruct the entire data cube. Assuming that compressed measurements are drawn from multiple subspaces, a novel algorithm was developed by solving a 1-norm minimization problem, which is known as reweighted sparse subspace clustering (RSSC). The proposed algorithm clusters the compressed measurements into different subspaces, which greatly improves the clustering accuracy over the SSC algorithm by adding a reweighted step. The compressed CASSI measurements obtained using the coherence-based coded aperture can improve the performance of the proposed spectral image clustering method. The accuracy of our spectral image clustering approach was verified through simulations on two real datasets.

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“…Sound source localization in the presence of interfering noise sources has been studied by applying classification on sources from individual directions [6,7]. In contrast to conventional speech/non-speech (SNS) classification problem, which takes a one-channel signal as input, the sound classification of multiple signals needs to extract the source signal from the mixed audio prior to applying classification.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to conventional speech/non-speech (SNS) classification problem, which takes a one-channel signal as input, the sound classification of multiple signals needs to extract the source signal from the mixed audio prior to applying classification. The methods for extraction include beamforming [7] and sound source separation by time-frequency masking [6]. Both methods apply disjoint source localization and classification.…”

Section: Introductionmentioning

confidence: 99%

Joint Localization and Classification of Multiple Sound Sources Using a Multi-task Neural Network

He¹,

Motlíček²,

Odobez³

2018

Interspeech 2018

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We propose a novel multi-task neural network-based approach for joint sound source localization and speech/non-speech classification in noisy environments. The network takes raw short time Fourier transform as input and outputs the likelihood values for the two tasks, which are used for the simultaneous detection, localization and classification of an unknown number of overlapping sound sources, Tested with real recorded data, our method achieves significantly better performance in terms of speech/non-speech classification and localization of speech sources, compared to method that performs localization and classification separately. In addition, we demonstrate that incorporating the temporal context can further improve the performance.

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Audio Tracking in Noisy Environments by Acoustic Map and Spectral Signature

Cited by 18 publications

References 36 publications

Acoustic Target Tracking Through a Cluster of Mobile Agents

Acoustic Target Tracking Through a Cluster of Mobile Agents

Classification of Remote Sensing Images Through Reweighted Sparse Subspace Representation Using Compressed Data

Joint Localization and Classification of Multiple Sound Sources Using a Multi-task Neural Network

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