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.

Authors: Reed-Jones, Jago T.; Jones, Karl O.; Fergus, Paul; Marsland, John; Ellis, David L.
Affiliations: Liverpool John Moores University, Liverpool, UK; Liverpool John Moores University, Liverpool, UK; Liverpool John Moores University, Liverpool, UK; Liverpool John Moores University, Liverpool, UK; Liverpool John Moores University, Liverpool, UK(See document for exact affiliation information.)
Express Paper 59; AES Convention 154; May 2023
Publication Date: May 13, 2023 Import into BibTeX
Subject: Spatial Audio
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=22084

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