An Indoor Sound Source Localization Dataset for Machine Learning

Wu, Tao; Jiang, Yong; Li, Nan; Tao, Feng

doi:10.1145/3297156.3297198

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…In their work, pre-recorded sound measurements and their corresponding source locations were used to train the multilevel B-Splines based learning model. A new dataset for learning-based SSL was proposed by [50] which contained different acoustic events recorded in the anechoic chamber, where the anechoic chamber environment was used to verify the feasibility of the proposed baseline model. Authors in [51] addressed SSL for indoor environments with high reverberation and low signal-to-noise ratio.…”

Section: A Related Workmentioning

confidence: 99%

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Free-Field TDOA-AOA Sound Source Localization Using Three Soundfield Microphones

et al. 2020

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Accurate source localization is an important problem in many research areas as well as practical applications in wireless communications and acoustic signal processing. This paper presents a passive three-dimensional sound source localization (SSL) method that employs a geometric configuration of three soundfield microphones. Two methods for estimating the angle of arrival (AOA) and time difference of arrival (TDOA) are proposed based on Ambisonics A and B format signals. The closed-form solution for sound source location estimation based on two TDOAs and three AOAs is derived. The proposed method is evaluated by simulations and physical experiments in our anechoic chamber. Simulations demonstrate that the estimation method can theoretically obtain Cramér-Rao lower bound for a small Gaussian noise present in AOA and TDOA observations. Investigation on the uncertainty of TDOA and AOA measurements depending on the length of measurement interval is also conducted. Experimental results in terms of RMSE indicate that the proposed solution can be used to accurately find a 3D position of the sound source in free-field environment. Performance evaluation regarding the number of estimation steps shows that higher accuracy can be achieved by longer observations of stationary sound source. INDEX TERMS 3D sound source localization, angle of arrival, cramér-rao bound, soundfiled microphone, time difference of arrival.

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Section: A Related Workmentioning

confidence: 99%

“…The third simulation shows the performance evaluation depending on the distance from the source for the fixed σ RD = 10 cm and σ AOA = 0.5 • . For this case the source moves away from the origin in accordance with the expression s = a [50,50,50] T where a ranges from 1 to 5. From Fig.…”

Section: A Ssl Simulation Under Gaussian Noisementioning

confidence: 99%

Free-Field TDOA-AOA Sound Source Localization Using Three Soundfield Microphones

et al. 2020

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“…A key factor in the advancement of these methods is development and release of audio-related datasets. There are many datasets for speech processing, including datasets with different settings and languages [Park and Mulc 2019], emotional speech [Tits et al 2019], speech source separation [Drude et al 2019], sound source localization [Wu et al 2018], noise suppression [Reddy et al 2020], background noise [Reddy et al 2019], music generation [Briot et al 2017], etc.…”

Section: Introductionmentioning

confidence: 99%

GWA: A Large High-Quality Acoustic Dataset for Audio Processing

Tang,

Aralikatti,

Ratnarajah

et al. 2022

Preprint

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Figure 1: Our IR data generation pipeline starts from a 3D model of a complex scene and its visual material annotations (unstructured texts). We sample multiple collision-free source and receiver locations in the scene. We use a novel scheme to automatically assign acoustic material parameters by semantic matching from a large acoustic database. Our hybrid acoustic simulator generates accurate impulse responses (IRs), which become part of the large synthetic impulse response dataset after post-processing.

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