Robust binaural localization of a target sound source by combining spectral source models and deep neural networks

Despite there being a clear evidence for top–down (e.g., attentional) effects in biological spatial hearing, relatively few machine hearing systems exploit the top–down model-based knowledge in sound localization. This paper addresses this issue by proposing a novel framework for the binaural sound localization that combines the model-based information about the spectral characteristics of sound sources and deep neural networks (DNNs). A target source model and a background source model are first estimated during a training phase using spectral features extracted from sound signals in isolation. When the identity of the background source is not available, a universal background model can be used. During testing, the source models are used jointly to explain the mixed observations and improve the localization process by selectively weighting source azimuth posteriors output by a DNN-based localization system. To address the possible mismatch between the training and testing, a model adaptation process is further employed the on-the-fly during testing, which adapts the background model parameters directly from the noisy observations in an iterative manner. The proposed system, therefore, combines the model-based and data-driven information flow within a single computational framework. The evaluation task involved localization of a target speech source in the presence of an interfering source and room reverberation. Our experiments show that by exploiting the model-based information in this way, the sound localization performance can be improved substantially under various noisy and reverberant conditions

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

Polyphonic Sound Event Detection by using Capsule Neural Networks

Artificial sound event detection (SED) has the aim to mimic the human ability to perceive and understand what is happening in the surroundings. Nowadays, Deep Learning offers valuable techniques for this goal such as Convolutional Neural Networks (CNNs). The Capsule Neural Network (CapsNet) architecture has been recently introduced in the image processing field with the intent to overcome some of the known limitations of CNNs, specifically regarding the scarce robustness to affine transformations (i.e., perspective, size, orientation) and the detection of overlapped images. This motivated the authors to employ CapsNets to deal with the polyphonic-SED task, in which multiple sound events occur simultaneously. Specifically, we propose to exploit the capsule units to represent a set of distinctive properties for each individual sound event. Capsule units are connected through a so-called "dynamic routing" that encourages learning part-whole relationships and improves the detection performance in a polyphonic context. This paper reports extensive evaluations carried out on three publicly available datasets, showing how the CapsNet-based algorithm not only outperforms standard CNNs but also allows to achieve the best results with respect to the state of the art algorithms

Exploiting Deep Neural Networks and Head Movements for Robust Binaural Localisation of Multiple Sources in Reverberant Environments

This paper presents a novel machine-hearing system that exploits deep neural networks (DNNs) and head movements for robust binaural localisation of multiple sources in reverberant environments. DNNs are used to learn the relationship between the source azimuth and binaural cues, consisting of the complete cross-correlation function (CCF) and interaural level differences (ILDs). In contrast to many previous binaural hearing systems, the proposed approach is not restricted to localisation of sound sources in the frontal hemifield. Due to the similarity of binaural cues in the frontal and rear hemifields, front-back confusions often occur. To address this, a head movement strategy is incorporated in the localisation model to help reduce the front-back errors. The proposed DNN system is compared to a Gaussian mixture model (GMM) based system that employs interaural time differences (ITDs) and ILDs as localisation features. Our experiments show that the DNN is able to exploit information in the CCF that is not available in the ITD cue, which together with head movements substantially improves localisation accuracies under challenging acoustic scenarios in which multiple talkers and room reverberation are present

Sound Event Localization, Detection, and Tracking by Deep Neural Networks

In this thesis, we present novel sound representations and classification methods for the task of sound event localization, detection, and tracking (SELDT). The human auditory system has evolved to localize multiple sound events, recognize and further track their motion individually in an acoustic environment. This ability of humans makes them context-aware and enables them to interact with their surroundings naturally. Developing similar methods for machines will provide an automatic description of social and human activities around them and enable machines to be context-aware similar to humans. Such methods can be employed to assist the hearing impaired to visualize sounds, for robot navigation, and to monitor biodiversity, the home, and cities. A real-life acoustic scene is complex in nature, with multiple sound events that are temporally and spatially overlapping, including stationary and moving events with varying angular velocities. Additionally, each individual sound event class, for example, a car horn can have a lot of variabilities, i.e., different cars have different horns, and within the same model of the car, the duration and the temporal structure of the horn sound is driver dependent. Performing SELDT in such overlapping and dynamic sound scenes while being robust is challenging for machines. Hence we propose to investigate the SELDT task in this thesis and use a data-driven approach using deep neural networks (DNNs). The sound event detection (SED) task requires the detection of onset and offset time for individual sound events and their corresponding labels. In this regard, we propose to use spatial and perceptual features extracted from multichannel audio for SED using two different DNNs, recurrent neural networks (RNNs) and convolutional recurrent neural networks (CRNNs). We show that using multichannel audio features improves the SED performance for overlapping sound events in comparison to traditional single-channel audio features. The proposed novel features and methods produced state-of-the-art performance for the real-life SED task and won the IEEE AASP DCASE challenge consecutively in 2016 and 2017. Sound event localization is the task of spatially locating the position of individual sound events. Traditionally, this has been approached using parametric methods. In this thesis, we propose a CRNN for detecting the azimuth and elevation angles of multiple temporally overlapping sound events. This is the first DNN-based method performing localization in complete azimuth and elevation space. In comparison to parametric methods which require the information of the number of active sources, the proposed method learns this information directly from the input data and estimates their respective spatial locations. Further, the proposed CRNN is shown to be more robust than parametric methods in reverberant scenarios. Finally, the detection and localization tasks are performed jointly using a CRNN. This method additionally tracks the spatial location with time, thus producing the SELDT results. This is the first DNN-based SELDT method and is shown to perform equally with stand-alone baselines for SED, localization, and tracking. The proposed SELDT method is evaluated on nine datasets that represent anechoic and reverberant sound scenes, stationary and moving sources with varying velocities, a different number of overlapping sound events and different microphone array formats. The results show that the SELDT method can track multiple overlapping sound events that are both spatially stationary and moving