High performance 3D sound localization for surveillance applications

Keyrouz, Fakheredine; Diepold, Klaus; Keyrouz, Shady

doi:10.1109/avss.2007.4425372

Cited by 12 publications

(6 citation statements)

References 8 publications

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“…Magnitude pairs for flat S Log-magnitude ratio (LMR) [12]: While the sourcecancellation method removes the dependence on signal S, the resulting features are complex, noisy, and difficult to interpret. This can be avoided by considering the magnitude representation which gives the relative per-frequency energy between the channel signals.…”

Section: Binaural Sound-source Invariant Featuresmentioning

confidence: 99%

“…Most closely related to our work are the source-cancellation and match-filtering algorithms [12], [13], [14], [15], where the binaural recordings (S L left, S R right ears) are represented as convolutions of a common sound-source signal S and the appropriate filters; for recording done in an anechoic space, these filters are the same-direction HRTFs (H L left, H R right ears). The per-frequency domain representation is given by…”

Section: A Prior Workmentioning

confidence: 99%

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Gaussian process models for HRTF based 3D sound localization

Luo

Zotkin

Duraiswami

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The human ability to localize sound-source direction using just two receivers is a complex process of direction inference from spectral cues of sound arriving at the ears. While these cues can be described using the well-known head-related transfer function (HRTF) concept, it is unclear as to how densely HRTF must be sampled and whether a higher-order representation is employed in localization. We propose a class of binaural sound source localization models to answer these two questions. First, using the sound received by two ears, we derive several binaural features that are invariant to the sound source signal. Second, these are implicitly mapped to a highdimensional reproducing kernel Hilbert space via a Gaussian process regression model for feature-direction tuples. Lastly, the features that are most relevant in the model are found via an efficient forward subset-selection method. Experimental results are shown for HRTFs belonging to the CIPIC database.

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Section: Binaural Sound-source Invariant Featuresmentioning

confidence: 99%

Section: A Prior Workmentioning

confidence: 99%

Gaussian process models for HRTF based 3D sound localization

Luo

Zotkin

Duraiswami

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Two important applications in intelligent audio surveillance are abnormal event detection [3,4] and sound source localization [13]. A comprehensive review of methods for audio surveillance has been recently published [2].…”

Section: Introductionmentioning

confidence: 99%

A real-time system for audio source localization with cheap sensor device

Saggese

Strisciuglio

Vento

et al. 2017

2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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We propose an architecture for real-time audio source localization based on the integration of localization methodologies within a framework that employs a cheap acquisition sensor. The architecture that we present takes as input the audio signals from two calibrated microphones. Then, it computes biological-inspired features of the sound signal and estimates its direction by means of a Gaussian Mixture Model estimator. We carried out an extensive experimental analysis on four data sets, one of which we realized and made publicly available. We evaluated several characteristics of the sound localization architecture and its use in real scenarios.

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“…It has been applied to many areas [2]- [5]. For good performance, sound signal obtained from the microphone must be well filtered and amplified.…”

Section: Introductionmentioning

confidence: 99%

A multi-objective covariance matrix adaptation evolutionary strategy based on decomposition for analog circuit design

Park¹,

Lee²

2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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Sound localization has been utilized in many fields. For good performance, sound signal obtained from the microphone has to be well filtered and amplified. Although filtering and amplification can be implemented in digital, analog circuit is needed because of sampling aliasing problem and limitation of computational resources in real-time application. The analog circuit consists of several components. Because all of possible combinations cannot be investigated, evolutionary algorithm (EA) is chosen to find promising solution candidates. In the paper, we built a multi-objective covariance adaptation evolutionary strategy based on decomposition to obtain the component values. Through the experimental results, our proposed algorithm showed good performance.Index Terms-State variable filter, covariance matrix adaptation evolution strategy, voltage level shifter, circuit optimization, multi-objective evolutionary algorithm, scalarization 978-1-4799-0224-8/13/$31.00 ©2013 IEEE

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High performance 3D sound localization for surveillance applications

Cited by 12 publications

References 8 publications

Gaussian process models for HRTF based 3D sound localization

Gaussian process models for HRTF based 3D sound localization

A real-time system for audio source localization with cheap sensor device

A multi-objective covariance matrix adaptation evolutionary strategy based on decomposition for analog circuit design

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