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Comparison of Performance in Binaural Sound Source Localisation using Convolutional Neural Networks for differing Feature Representations

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Binaural Sound Source Localisation is increasingly being achieved by means of the Convolutional Neural Network (CNN). These networks take in a Time-Frequency representation of audio as an input, and use this to estimate the direction of arrival of a sound. In previous works, different Time-Frequency representations have been used, but never only using solely magnitude spectra, leading to a lack of understanding in the importance of this in full azimuthal binaural sound source localisation. This work aims to address that gap by testing the performance of a CNN trained and tested on four different Time-Frequency representations: Mel-Spectrogram, Gammatonegram, Mel-Frequency Cepstrum, and Gammatone-Frequency Cepstrum. From this test, it was found that Spectrograms are suitable for the task of full azimuthal binaural sound source localisation.

Express Paper 59; AES Convention 154; May 2023
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