Position estimation of binaural sound source in reverberant environments

Ghamdan, Lama; Shoman, Mahmoud; Elwahab, Reda Abd; Ghamry, Nivin A.

doi:10.1016/j.eij.2016.05.002

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…These systems encompass a range of setups, such as intelligent loudspeakers [12], spherical microphones [13], triangular configu-rations [14], and arrays of acoustic sensors [15]. Simpler audio formats including binaural recordings have been investigated to a much lesser extent, including few studies with classical machine learning methods [4], [16] and very limited research related to deep learning [7], [8].…”

Section: Related Workmentioning

confidence: 99%

Speaker Distance Estimation in Enclosures From Single-Channel Audio

Neri,

Politis,

Krause

et al. 2024

IEEE/ACM Trans. Audio Speech Lang. Process.

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Distance estimation from audio plays a crucial role in various applications, such as acoustic scene analysis, sound source localization, and room modeling. Most studies predominantly center on employing a classification approach, where distances are discretized into distinct categories, enabling smoother model training and achieving higher accuracy but imposing restrictions on the precision of the obtained sound source position. Towards this direction, in this paper we propose a novel approach for continuous distance estimation from audio signals using a convolutional recurrent neural network with an attention module. The attention mechanism enables the model to focus on relevant temporal and spectral features, enhancing its ability to capture fine-grained distance-related information. To evaluate the effectiveness of our proposed method, we conduct extensive experiments using audio recordings in controlled environments with three levels of realism (synthetic room impulse response, measured response with convolved speech, and real recordings) on four datasets (our synthetic dataset, QMULTI-MIT, VoiceHome-2, and STARSS23). Experimental results show that the model achieves an absolute error of 0.11 meters in a noiseless synthetic scenario. Moreover, the results showed an absolute error of about 1.30 meters in the hybrid scenario. The algorithm's performance in the real scenario, where unpredictable environmental factors and noise are prevalent, yields an absolute error of approximately 0.50 meters. For reproducible research purposes we make model, code, and synthetic datasets available at https://github.com/michaelneri/audio-distance-estimation.

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Section: Related Workmentioning

confidence: 99%

Speaker Distance Estimation in Enclosures From Single-Channel Audio

Neri,

Politis,

Krause

et al. 2024

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…Although the study provided directional azimuth information as explicitly given values, without joint modeling, it showed a strong interdependence between distance and localization cues. Ghamdan et al [54] used GMMs to classify the sound source position defined as azimuth and distance, from a finite set of classes. The study was limited to [-90º, 90º] degrees in the azimuth plane and a set of three distances in the close area up to 2 meters.…”

Section: Binaural Source Distance and Doa Estimationmentioning

confidence: 99%

Binaural Sound Source Distance Estimation and Localization for a Moving Listener

Krause,

García-Barrios,

Politis

et al. 2024

IEEE/ACM Trans. Audio Speech Lang. Process.

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In this paper, we investigate the tasks of binaural source distance estimation (SDE) and direction-of-arrival estimation (DOAE) using motion-based cues in a scenario with a walking listener. On top of performing both tasks as separate problems, we study two methods of solving the joint task of simultaneous source distance estimation and localization (SDEL), with a single model. Experiments are conducted for three different scenarios: a static receiver; a static receiver with a rotating head; and a freely moving listener inside a room. The study proposes rotation and translation features to include information about the receiver's motion during model training and studies the effects of these on the final performance. The work includes extended simulation of three datasets containing numerous testing scenarios for sound sources, covering a wide range of DOAs and a source-to-receiver distance up to 15 m. Results are further analyzed with respect to room reverberation, walking speed, as well as source-to-receiver distance. The presented outcomes show large improvements in both DOA and distance estimation for a model that uses motion-based cues as compared with a static scenario. These include a decrease of 9.50°in DOA and 1.56m in distance errors for a joint model, followed by 16.17°and 0.17m for separate models.

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“…Fischer et al [21] modified the cross-correlation matrix of the array sound pressure signal to effectively improve the beamforming accuracy in the reverberant environment. Based on the statistical characteristics of binaural signals and the difference in amplitude spectrum, Ghamdan et al [22] proposed a new algorithm. This algorithm can be applied to different reverberation rooms and weaken the dependence on relevant environmental parameters.…”

Section: Introductionmentioning

confidence: 99%

An improved deconvolution beamforming algorithm for acoustic imaging of low signal-to-noise ratio sound sources in reverberant field

Guo¹,

Chen²,

Xia³

et al. 2023

J. vibroeng.

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Most of the existing acoustic imaging studies in reverberant field ignore the influence of signal-to-noise ratio. As a result, commonly used beamforming algorithms in reverberant backgrounds have poor imaging accuracy for low signal-to-noise ratio sound sources. In response to that problem, an improved adaptive beamforming algorithm called SC-DAMAS is put forward in this paper. The algorithm replaces the free-field Green's function with the impulse response function, making the algorithm more suitable for acoustic imaging of low signal-to-noise ratio in a reverberant environment. Besides, the comparative simulation results with the conventional beamforming method and orthogonal matching pursuit algorithm-based DAMAS, as well as sound source acoustic imaging experiments are carried out to analyze its effectiveness. It is indicated that, in the reverberation field, the SC-DAMAS has no obvious sidelobes and achieves higher positioning accuracy for acoustic imaging of low signal-to-noise ratio sound source than the abovementioned counterparts, and its imaging test result is consistent with the actual situation, which verifies the effectiveness of the algorithm.

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Position estimation of binaural sound source in reverberant environments

Cited by 5 publications

References 15 publications

Speaker Distance Estimation in Enclosures From Single-Channel Audio

Speaker Distance Estimation in Enclosures From Single-Channel Audio

Binaural Sound Source Distance Estimation and Localization for a Moving Listener

An improved deconvolution beamforming algorithm for acoustic imaging of low signal-to-noise ratio sound sources in reverberant field

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