Dual input neural networks for positional sound source localization
This addresses the problem of accurately localizing sound sources for signal processing applications, representing an incremental improvement over existing methods.
The paper tackled sound source localization by introducing Dual Input Neural Networks (DI-NNs) to combine high-dimensional audio signals with scene metadata, achieving a five times lower localization error than a Least-Squares method and two times lower than a Convolutional Recurrent Neural Network in real recordings.
In many signal processing applications, metadata may be advantageously used in conjunction with a high dimensional signal to produce a desired output. In the case of classical Sound Source Localization (SSL) algorithms, information from a high dimensional, multichannel audio signals received by many distributed microphones is combined with information describing acoustic properties of the scene, such as the microphones' coordinates in space, to estimate the position of a sound source. We introduce Dual Input Neural Networks (DI-NNs) as a simple and effective way to model these two data types in a neural network. We train and evaluate our proposed DI-NN on scenarios of varying difficulty and realism and compare it against an alternative architecture, a classical Least-Squares (LS) method as well as a classical Convolutional Recurrent Neural Network (CRNN). Our results show that the DI-NN significantly outperforms the baselines, achieving a five times lower localization error than the LS method and two times lower than the CRNN in a test dataset of real recordings.