Towards End-to-End Acoustic Localization using Deep Learning: from Audio Signal to Source Position Coordinates

This paper presents a novel approach for indoor acoustic source localization using microphone arrays and based on a Convolutional Neural Network (CNN). The proposed solution is, to the best of our knowledge, the first published work in which the CNN is designed to directly estimate the three dimensional position of an acoustic source, using the raw audio signal as the input information avoiding the use of hand crafted audio features. Given the limited amount of available localization data, we propose in this paper a training strategy based on two steps. We first train our network using semi-synthetic data, generated from close talk speech recordings, and where we simulate the time delays and distortion suffered in the signal that propagates from the source to the array of microphones. We then fine tune this network using a small amount of real data. Our experimental results show that this strategy is able to produce networks that significantly improve existing localization methods based on \textit{SRP-PHAT} strategies. In addition, our experiments show that our CNN method exhibits better resistance against varying gender of the speaker and different window sizes compared with the other methods.Comment: 18 pages, 3 figures, 8 table

Spatial dissection of a soundfield using spherical harmonic decomposition

A real-world soundfield is often contributed by multiple desired and undesired sound sources. The performance of many acoustic systems such as automatic speech recognition, audio surveillance, and teleconference relies on its ability to extract the desired sound components in such a mixed environment. The existing solutions to the above problem are constrained by various fundamental limitations and require to enforce different priors depending on the acoustic condition such as reverberation and spatial distribution of sound sources. With the growing emphasis and integration of audio applications in diverse technologies such as smart home and virtual reality appliances, it is imperative to advance the source separation technology in order to overcome the limitations of the traditional approaches. To that end, we exploit the harmonic decomposition model to dissect a mixed soundfield into its underlying desired and undesired components based on source and signal characteristics. By analysing the spatial projection of a soundfield, we achieve multiple outcomes such as (i) soundfield separation with respect to distinct source regions, (ii) source separation in a mixed soundfield using modal coherence model, and (iii) direction of arrival (DOA) estimation of multiple overlapping sound sources through pattern recognition of the modal coherence of a soundfield. We first employ an array of higher order microphones for soundfield separation in order to reduce hardware requirement and implementation complexity. Subsequently, we develop novel mathematical models for modal coherence of noisy and reverberant soundfields that facilitate convenient ways for estimating DOA and power spectral densities leading to robust source separation algorithms. The modal domain approach to the soundfield/source separation allows us to circumvent several practical limitations of the existing techniques and enhance the performance and robustness of the system. The proposed methods are presented with several practical applications and performance evaluations using simulated and real-life dataset

Semi-Supervised Sound Source Localization Based on Manifold Regularization

Conventional speaker localization algorithms, based merely on the received microphone signals, are often sensitive to adverse conditions, such as: high reverberation or low signal to noise ratio (SNR). In some scenarios, e.g. in meeting rooms or cars, it can be assumed that the source position is confined to a predefined area, and the acoustic parameters of the environment are approximately fixed. Such scenarios give rise to the assumption that the acoustic samples from the region of interest have a distinct geometrical structure. In this paper, we show that the high dimensional acoustic samples indeed lie on a low dimensional manifold and can be embedded into a low dimensional space. Motivated by this result, we propose a semi-supervised source localization algorithm which recovers the inverse mapping between the acoustic samples and their corresponding locations. The idea is to use an optimization framework based on manifold regularization, that involves smoothness constraints of possible solutions with respect to the manifold. The proposed algorithm, termed Manifold Regularization for Localization (MRL), is implemented in an adaptive manner. The initialization is conducted with only few labelled samples attached with their respective source locations, and then the system is gradually adapted as new unlabelled samples (with unknown source locations) are received. Experimental results show superior localization performance when compared with a recently presented algorithm based on a manifold learning approach and with the generalized cross-correlation (GCC) algorithm as a baseline