Use of the crosspower-spectrum phase in acoustic event location

Omologo, Maurizio; Svaizer, Piergiorgio

doi:10.1109/89.568735

Cited by 181 publications

(87 citation statements)

References 7 publications

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“…Also, it has been recently proven that, in case of high signal-to-noise ratio, the GCC-PHAT function is the optimal time-delay estimator in a maximumlikelihood sense, regardless of the amount of reverberation [25]. Aside from that, many comparative studies proved its robustness in the presence of multipath distorsion (see, for instance, [26] and [27]). …”

Section: B Time-delay Estimationmentioning

confidence: 99%

Observation of Vehicle Axles Through Pass-by Noise: A Strategy of Microphone Array Design

Marmaroli

Carmona

Odobez

et al. 2013

IEEE Trans. Intell. Transport. Syst.

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Abstract-This paper focuses on road traffic monitoring using sounds and proposes, more specifically, a microphone array design methodology for observing vehicle trajectory from acoustic-based correlation functions. In a former work, authors have shown that combining generalized cross correlation (GCC) functions and a particle filter onto the audio signals simultaneously acquired by two sensors placed near the road allows the joint estimation of the speed and the wheelbase length of road vehicles as they pass by. This is mainly due to the broadband nature of the tire/road noise, which makes their spatial dissociation possible by means of an appropriate GCC processor. At the time, nothing has been said about the best distance to chose between the sensors. A methodology is proposed here to find this optimum, which is expected to improve the observation quality and, thus, the tracking performance. Theoretical developments of this paper are partially assessed with preliminary experiments.

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Section: B Time-delay Estimationmentioning

confidence: 99%

Observation of Vehicle Axles Through Pass-by Noise: A Strategy of Microphone Array Design

Marmaroli

Carmona

Odobez

et al. 2013

IEEE Trans. Intell. Transport. Syst.

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“…It was later shown [4], [16], [17], [18] that it is more robust and reliable in realistic reverberant conditions than other weighting functions designed to be statistically optimal under specific non-reverberant noise conditions. The SRP-PHAT algorithm [4], [19] applies the PHAT weighting in the context of the filter-and-sum beamformer.…”

Section: Srp-phat Localizationmentioning

confidence: 99%

Accelerated Speech Source Localization via a Hierarchical Search of Steered Response Power

Zotkin

Duraiswami

2004

IEEE Trans. Speech Audio Process.

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Accurate and fast localization of multiple speech sound sources is a problem of significant interest in applications such as conferencing systems. Recently approaches that are based on search for local peaks of the steered response power are becoming popular, despite their known computational expense. Based on the observation that the wavelengths of the sound from a speech source are comparable to the dimensions of the space being searched, and that the source is broadband, we develop an efficient search algorithm. Significant speedups are achieved by using coarse-to-fine strategies in both space and frequency. We present applications of the search algorithm to speed up simple delay-and-sum beamforming and SRP-PHAT source localization algorithms. A systematic series of comparisons with previous algorithms are made that show that the technique is much faster, is robust and accurate.The performance of the algorithm can further be improved by using constraints from computer vision.

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“…Many methods for sound source localization use the time difference of arrival of a speech signal at the microphones positioned spatially. Omologo and Svaizer calculated the direction of arrival of the speech from the time difference of arrival between two microphones and localized the sound source in two-dimensional space [19]. Brandstein and colleagues proposed a method that uses the time difference of arrival of the speech to estimate the position of the sound source in three-dimensional space [18].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a great deal of research has addressed sound source localization by using microphone arrays [18][19][20][21][22][23]. The understanding of the spatial position of sound is used in noise reduction, sound source tracking, and speech emphasis.…”

Section: Introductionmentioning

confidence: 99%

Multichannel speech separation and localization by frequency assignment

Ihara

Handa

Nagai

et al. 2006

Electron Comm Jpn Pt III

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SUMMARYIn this paper, we propose frequency assignment as a method for multichannel speech separation. In this method, the magnitudes of the absolute values of each component in the frequency domain are compared, then based on this comparison result determines to which channel a frequency component originally belongs. The speech is separated by assigning the frequency to the determined channel. Therefore, it becomes possible to separate multichannel speech with a low computational load. However, it has the restriction that each speaker must speak close to his microphone. For situations in which this premise does not hold, we present a solution that uses beam-forming toward the sound sources. We also show the application of the frequency assignment to sound source localization. In this paper, we demonstrate the usefulness of the proposed method through simulation tests and tests in a realistic environment.

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Use of the crosspower-spectrum phase in acoustic event location

Cited by 181 publications

References 7 publications

Observation of Vehicle Axles Through Pass-by Noise: A Strategy of Microphone Array Design

Observation of Vehicle Axles Through Pass-by Noise: A Strategy of Microphone Array Design

Accelerated Speech Source Localization via a Hierarchical Search of Steered Response Power

Multichannel speech separation and localization by frequency assignment

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