VON MISES PRIOR FOR PHASE-NOISY DOA ESTIMATION: THE VITAMIN ALGORITHM

International audienceSound waves in the ocean are affected by the space and time variabilities of the propagation medium. These fluctuations, mainly caused by internal waves such as tides and gyres, can lead to a loss of phase information in measured wave-fronts, and make hardly predictable the true location of a source. As a consequence, the performance of classical direction of arrival (DOA) estimation algorithms are significantly degraded. An important literature addresses this issue by considering either the phase as non-informative or the environment as a noise with no physical information. In this work, we propose to introduce a phase prior inspired by random fluctuation theories. This prior is combined with a sparsity assumption on the number of expected DOAs and exploited within a Bayesian framework. The contributions of such an approach are twofold: by the use of suitable prior information (small number of DOAs and phase distortion), it allows an estimation of DOAs from a single snapshot , while simultaneously providing a posterior estimation of the mean fluctuations of the propagation medium. Bayesian inference can be performed in different ways. Among the different possible procedures, we chose here to resort to a Bethe approximation and a message-passing approach recently considered in compressive sensing setups. The resulting algorithm places in the continuation of our previous works. The main improvement lies in the proba-bilistic model used to describe the phase distortion. Here we use a Multivariate Von Mises distribution, more suitable to directional statistics and still fitting the simplified theory of phase fluctuation. Numerical experiments with synthetic datasets show that the proposed algorithm , dubbed as VITAMIN for ``Von mIses swepT Approximate Message passINg'', presents interesting performance compared to other state-of-the-art algorithms. In particular, in the considered experiments, VITAMIN behaves well regarding its robustness to additive noise and phase fluctuations

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

Location-free Spectrum Cartography

Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using spatially distributed sensor measurements. Applications of these maps include network planning, interference coordination, power control, localization, and cognitive radios to name a few. Since existing spectrum cartography techniques require accurate estimates of the sensor locations, their performance is drastically impaired by multipath affecting the positioning pilot signals, as occurs in indoor or dense urban scenarios. To overcome such a limitation, this paper introduces a novel paradigm for spectrum cartography, where estimation of spectral maps relies on features of these positioning signals rather than on location estimates. Specific learning algorithms are built upon this approach and offer a markedly improved estimation performance than existing approaches relying on localization, as demonstrated by simulation studies in indoor scenarios.Comment: 14 pages, 12 figures, 1 table. Submitted to IEEE Transactions on Signal Processin

Influence of Lossy Speech Codecs on Hearing-aid, Binaural Sound Source Localisation using DNNs

Hearing aids are typically equipped with multiple microphones to exploit spatial information for source localisation and speech enhancement. Especially for hearing aids, a good source localisation is important: it not only guides source separation methods but can also be used to enhance spatial cues, increasing user-awareness of important events in their surroundings. We use a state-of-the-art deep neural network (DNN) to perform binaural direction-of-arrival (DoA) estimation, where the DNN uses information from all microphones at both ears. However, hearing aids have limited bandwidth to exchange this data. Bluetooth low-energy (BLE) is emerging as an attractive option to facilitate such data exchange, with the LC3plus codec offering several bitrate and latency trade-off possibilities. In this paper, we investigate the effect of such lossy codecs on localisation accuracy. Specifically, we consider two conditions: processing at one ear vs processing at a central point, which influences the number of channels that need to be encoded. Performance is benchmarked against a baseline that allows full audio-exchange - yielding valuable insights into the usage of DNNs under lossy encoding. We also extend the Pyroomacoustics library to include hearing-device and head-related transfer functions (HD-HRTFs) to suitably train the networks. This can also benefit other researchers in the field