Binaural sound source localization is the task of finding the location of a sound source using binaural audio as affected by the head-related transfer functions (HRTFs) of a binaural array. The most common approach to this is to train a convolutional neural network directly on the magnitude and phase of the binaural audio. Recurrent layers can then also be introduced to allow for consideration of the temporal context of the binaural data, as to create a convolutional recurrent neural network (CRNN).
This work compares the relative performance of this approach for speech localization on the horizontal plane using four different CRNN models based on different types of recurrent layers; Conv-GRU, Conv-BiGRU, Conv-LSTM, and Conv-BiLSTM, as well as a baseline system of a more conventional CNN with no recurrent layers. These systems were trained and tested on datasets of binaural audio created by convolution of speech samples with BRIRs of 120 rooms, for 50 azimuthal directions. Additive noise created from additional sound sources on the horizontal plane were also added to the signal.
Results show a clear preference for use of CRNN over CNN, with overall localization error and front-back confusion being reduced, with it additionally being seen that such systems are less effected by increasing reverb time and reduced signal to noise ratio. Comparing the recurrent layers also reveals that LSTM based layers see the best overall localisation performance, while layers with bidirectionality are more robust, and so overall finding a preference for Conv-BiLSTM for the task.
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https://aes2.org/publications/elibrary-page/?id=22747
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