A Novel Approach for Hardware Based Sound Localization

Kugler, Mauricio; Takanobu, Hideaki; Kuroyanagi, Sachiko; Iwata, Atsushi

doi:10.1007/978-3-642-15819-3_65

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Other alternatives for combining the angular values of each frequency channel exist. For instance, a classifier can be used to learn the patterns of all channels and output the final direction, as previously proposed in [38]. However, due to the high dimensionality, such classifier is very hard to learn.…”

Section: Proposed Systemmentioning

confidence: 99%

Design of a Compact Sound Localization Device on a Stand-Alone FPGA-Based Platform

Kugler

Tossavainen

Kuroyanagi

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYSound localization systems are widely studied and have several potential applications, including hearing aid devices, surveillance and robotics. However, few proposed solutions target portable systems, such as wearable devices, which require a small unnoticeable platform, or unmanned aerial vehicles, in which weight and low power consumption are critical aspects. The main objective of this research is to achieve real-time sound localization capability in a small, self-contained device, without having to rely on large shaped platforms or complex microphone arrays. The proposed device has two surface-mount microphones spaced only 20 mm apart. Such reduced dimensions present challenges for the implementation, as differences in level and spectra become negligible, and only timedifference of arrival (TDoA) can be used as a localization cue. Three main issues have to be addressed in order to accomplish these objectives. To achieve real-time processing, the TDoA is calculated using zero-crossing spikes applied to the hardware-friendly Jeffers model. In order to make up for the reduction in resolution due to the small dimensions, the signal is upsampled several-fold within the system. Finally, a coherence-based spectral masking is used to select only frequency components with relevant TDoA information. The proposed system was implemented on a fieldprogrammable gate array (FPGA) based platform, due to the large amount of concurrent and independent tasks, which can be efficiently parallelized in reconfigurable hardware devices. Experimental results with white-noise and environmental sounds show high accuracies for both anechoic and reverberant conditions.

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Section: Proposed Systemmentioning

confidence: 99%

Design of a Compact Sound Localization Device on a Stand-Alone FPGA-Based Platform

Kugler

Tossavainen

Kuroyanagi

et al. 2016

IEICE Trans. Inf. & Syst.

Self Cite

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“…Among these, the most popular are the Radial Basis Function (RBF) networks used with microphone arrays [5], [6]. Neural networks have also been applied in a two-microphone configuration [7]. Complex-valued neural networks have been used in combination with microphone arrays for sound localization [8], however, the potential for their use in a twomicrophone configuration is yet to be explored.…”

Section: Introductionmentioning

confidence: 99%

An Approach for Sound Source Localization by Complex-Valued Neural Network

Tsuzuki

Kugler

Kuroyanagi

et al. 2013

IEICE Trans. Inf. & Syst.

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Hirofumi TSUZUKI†a) , Student Member, Mauricio KUGLER †b) , Nonmember, Susumu KUROYANAGI †c) , and Akira IWATA †d) , Members SUMMARY This paper presents a Complex-Valued Neural Networkbased sound localization method. The proposed approach uses two microphones to localize sound sources in the whole horizontal plane. The method uses time delay and amplitude difference to generate a set of features which are then classified by a Complex-Valued Multi-Layer Perceptron. The advantage of using complex values is that the amplitude information can naturally masks the phase information. The proposed method is analyzed experimentally with regard to the spectral characteristics of the target sounds and its tolerance to noise.

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音像仰角推定に関する両耳聴モデル改善の基礎検討

Sakamoto

2023

Transactions of Japan Society of Kansei Engineering

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In this study, the binaural model for the sound image elevation estimation was extended and experimentally verified under adverse perceptual conditions in a car cabin. First, the relationship between the spectrum of left and right ear input signals and sound image elevation in a simulation of a car cabin environment and in an actual car cabin environment was learned using a neural network. Then, cross validation of the proposed method was conducted under the conditions of a sound field where detection and discrimination of the spectral notches, an important cue for sound source elevation perception, is difficult, and as a result, high generalization performance was obtained. The model was then further extended and its accuracy was again tested in the interior environment of a car with noise. Results verified that the proposed approach exhibited the same trends as the conventional method.

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A Novel Approach for Hardware Based Sound Localization

Cited by 3 publications

References 8 publications

Design of a Compact Sound Localization Device on a Stand-Alone FPGA-Based Platform

Design of a Compact Sound Localization Device on a Stand-Alone FPGA-Based Platform

An Approach for Sound Source Localization by Complex-Valued Neural Network

音像仰角推定に関する両耳聴モデル改善の基礎検討

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