A Compressed Encoding Scheme for Approximate Tdoa Estimation

Vargas, Elizabeth; Hopgood, James R.; Brown, Keith; Subr, Kartic

doi:10.23919/eusipco.2018.8553197

Cited by 2 publications

(1 citation statement)

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“…Traditional methods for performing ASL have been widely studied in the literature [4], the most common of which are: (i) Time Difference of Arrival (TDoA)-based approaches, which normally employ Generalized Cross-Correlation (GCC) methods [25], [47], [48]; (ii) beamforming-based approaches, E. Vargas and K. Brown are with the Institute of Sensors, Signals, and Systems, Heriot-Watt University, Email: elizabeth.vargas@hw.ac.uk James R. Hopgood is with the Institute of Digital Communications, in the School of Engineering, University of Edinburgh. K. Subr is with the Institute of Perception, Action and Behaviour, University of Edinburgh.…”

Section: Introductionmentioning

confidence: 99%

On Improved Training of CNN for Acoustic Source Localisation

Vargas

Hopgood

Brown

et al. 2021

IEEE/ACM Trans. Audio Speech Lang. Process.

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Convolutional Neural Networks (CNNs) are a popular choice for estimating Direction of Arrival (DoA) without explicitly estimating delays between multiple microphones. The CNN method first optimises unknown filter weights (of a CNN) by using observations and ground-truth directional information. This trained CNN is then used to predict incident directions given test observations. Most existing methods train using spectrallyflat random signals and test using speech. In this paper, which focuses on single source DoA estimation, we find that training with speech or music signals produces a relative improvement in DoA accuracy for a variety of audio classes across 16 acoustic conditions and 9 DoAs, amounting to an average improvement of around 17% and 19% respectively when compared to training with spectrally flat random signals. This improvement is also observed in scenarios in which the speech and music signals are synthesised using, for example, a Generative Adversarial Network (GAN). When the acoustic environments during test and training are similar and reverberant, training a CNN with speech outperforms Generalized Cross Correlation (GCC) methods by about 125%. When the test conditions are different, a CNN performs comparably. This paper takes a step towards answering open questions in the literature regarding the nature of the signals used during training, as well as the amount of data required for estimating DoA using CNNs.

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