Robust Source Localization in Reverberant Environments Based on Weighted Fuzzy Clustering

Successful localization of sound sources in reverberant enclosures is an important prerequisite for many spatial signal processing algorithms. We investigate the use of a weighted fuzzy-means cluster algorithm for robust source localization using location cues extracted from a microphone array. In orderto increase the algorithm's robustness against sound reflections, we incorporate observation weights to emphasize reliable cues over unreliable ones. The weights are computed from local feature statistics around sound onsets because it is known that these regions are least affected by reverberation. Experimental results illustrate the superiority of the method when compared with standard fuzzy clustering. The proposed algorithm successfully located two speech sources for a range of angular separations in room environments with reverberation times of up to 600 ms

Evaluations on underdetermined blind source separation in adverse environments using time-frequency masking

The successful implementation of speech processing systems in the real world depends on its ability to handle adverse acoustic conditions with undesirable factors such as room reverberation and background noise. In this study, an extension to the established multiple sensors degenerate unmixing estimation technique (MENUET) algorithm for blind source separation is proposed based on the fuzzy c-means clustering to yield improvements in separation ability for underdetermined situations using a nonlinear microphone array. However, rather than test the blind source separation ability solely on reverberant conditions, this paper extends this to include a variety of simulated and real-world noisy environments. Results reported encouraging separation ability and improved perceptual quality of the separated sources for such adverse conditions. Not only does this establish this proposed methodology as a credible improvement to the system, but also implies further applicability in areas such as noise suppression in adverse acoustic environments

Acoustic sensor network geometry calibration and applications

In the modern world, we are increasingly surrounded by computation devices with communication links and one or more microphones. Such devices are, for example, smartphones, tablets, laptops or hearing aids. These devices can work together as nodes in an acoustic sensor network (ASN). Such networks are a growing platform that opens the possibility for many practical applications. ASN based speech enhancement, source localization, and event detection can be applied for teleconferencing, camera control, automation, or assisted living. For this kind of applications, the awareness of auditory objects and their spatial positioning are key properties. In order to provide these two kinds of information, novel methods have been developed in this thesis. Information on the type of auditory objects is provided by a novel real-time sound classification method. Information on the position of human speakers is provided by a novel localization and tracking method. In order to localize with respect to the ASN, the relative arrangement of the sensor nodes has to be known. Therefore, different novel geometry calibration methods were developed. Sound classification The first method addresses the task of identification of auditory objects. A novel application of the bag-of-features (BoF) paradigm on acoustic event classification and detection was introduced. It can be used for event and speech detection as well as for speaker identification. The use of both mel frequency cepstral coefficient (MFCC) and Gammatone frequency cepstral coefficient (GFCC) features improves the classification accuracy. By using soft quantization and introducing supervised training for the BoF model, superior accuracy is achieved. The method generalizes well from limited training data. It is working online and can be computed in a fraction of real-time. By a dedicated training strategy based on a hierarchy of stationarity, the detection of speech in mixtures with noise was realized. This makes the method robust against severe noises levels corrupting the speech signal. Thus it is possible to provide control information to a beamformer in order to realize blind speech enhancement. A reliable improvement is achieved in the presence of one or more stationary noise sources. Speaker localization The localization method enables each node to determine the direction of arrival (DoA) of concurrent sound sources. The author's neuro-biologically inspired speaker localization method for microphone arrays was refined for the use in ASNs. By implementing a dedicated cochlear and midbrain model, it is robust against the reverberation found in indoor rooms. In order to better model the unknown number of concurrent speakers, an application of the EM algorithm that realizes probabilistic clustering according to auditory scene analysis (ASA) principles was introduced. Based on this approach, a system for Euclidean tracking in ASNs was designed. Each node applies the node wise localization method and shares probabilistic DoA estimates together with an estimate of the spectral distribution with the network. As this information is relatively sparse, it can be transmitted with low bandwidth. The system is robust against jitter and transmission errors. The information from all nodes is integrated according to spectral similarity to correctly associate concurrent speakers. By incorporating the intersection angle in the triangulation, the precision of the Euclidean localization is improved. Tracks of concurrent speakers are computed over time, as is shown with recordings in a reverberant room. Geometry calibration The central task of geometry calibration has been solved with special focus on sensor nodes equipped with multiple microphones. Novel methods were developed for different scenarios. An audio-visual method was introduced for the calibration of ASNs in video conferencing scenarios. The DoAs estimates are fused with visual speaker tracking in order to provide sensor positions in a common coordinate system. A novel acoustic calibration method determines the relative positioning of the nodes from ambient sounds alone. Unlike previous methods that only infer the positioning of distributed microphones, the DoA is incorporated and thus it becomes possible to calibrate the orientation of the nodes with a high accuracy. This is very important for all applications using the spatial information, as the triangulation error increases dramatically with bad orientation estimates. As speech events can be used, the calibration becomes possible without the requirement of playing dedicated calibration sounds. Based on this, an online method employing a genetic algorithm with incremental measurements was introduced. By using the robust speech localization method, the calibration is computed in parallel to the tracking. The online method is be able to calibrate ASNs in real time, as is shown with recordings of natural speakers in a reverberant room. The informed acoustic sensor network All new methods are important building blocks for the use of ASNs. The online methods for localization and calibration both make use of the neuro-biologically inspired processing in the nodes which leads to state-of-the-art results, even in reverberant enclosures. The high robustness and reliability can be improved even more by including the event detection method in order to exclude non-speech events. When all methods are combined, both semantic information on what is happening in the acoustic scene as well as spatial information on the positioning of the speakers and sensor nodes is automatically acquired in real time. This realizes truly informed audio processing in ASNs. Practical applicability is shown by application to recordings in reverberant rooms. The contribution of this thesis is thus not only to advance the state-of-the-art in automatically acquiring information on the acoustic scene, but also pushing the practical applicability of such methods

An Online Solution for Localisation, Tracking and Separation of Moving Speech Sources

The problem of separating a time varying number of speech sources in a room is difficult to solve. The challenge lies in estimating the number and the location of these speech sources. Furthermore, the tracked speech sources need to be separated. This thesis proposes a solution which utilises the Random Finite Set approach to estimate the number and location of these speech sources and subsequently separate the speech source mixture via time frequency masking

Acoustic event detection and localization using distributed microphone arrays

Automatic acoustic scene analysis is a complex task that involves several functionalities: detection (time), localization (space), separation, recognition, etc. This thesis focuses on both acoustic event detection (AED) and acoustic source localization (ASL), when several sources may be simultaneously present in a room. In particular, the experimentation work is carried out with a meeting-room scenario. Unlike previous works that either employed models of all possible sound combinations or additionally used video signals, in this thesis, the time overlapping sound problem is tackled by exploiting the signal diversity that results from the usage of multiple microphone array beamformers. The core of this thesis work is a rather computationally efficient approach that consists of three processing stages. In the first, a set of (null) steering beamformers is used to carry out diverse partial signal separations, by using multiple arbitrarily located linear microphone arrays, each of them composed of a small number of microphones. In the second stage, each of the beamformer output goes through a classification step, which uses models for all the targeted sound classes (HMM-GMM, in the experiments). Then, in a third stage, the classifier scores, either being intra- or inter-array, are combined using a probabilistic criterion (like MAP) or a machine learning fusion technique (fuzzy integral (FI), in the experiments). The above-mentioned processing scheme is applied in this thesis to a set of complexity-increasing problems, which are defined by the assumptions made regarding identities (plus time endpoints) and/or positions of sounds. In fact, the thesis report starts with the problem of unambiguously mapping the identities to the positions, continues with AED (positions assumed) and ASL (identities assumed), and ends with the integration of AED and ASL in a single system, which does not need any assumption about identities or positions. The evaluation experiments are carried out in a meeting-room scenario, where two sources are temporally overlapped; one of them is always speech and the other is an acoustic event from a pre-defined set. Two different databases are used, one that is produced by merging signals actually recorded in the UPC¿s department smart-room, and the other consists of overlapping sound signals directly recorded in the same room and in a rather spontaneous way. From the experimental results with a single array, it can be observed that the proposed detection system performs better than either the model based system or a blind source separation based system. Moreover, the product rule based combination and the FI based fusion of the scores resulting from the multiple arrays improve the accuracies further. On the other hand, the posterior position assignment is performed with a very small error rate. Regarding ASL and assuming an accurate AED system output, the 1-source localization performance of the proposed system is slightly better than that of the widely-used SRP-PHAT system, working in an event-based mode, and it even performs significantly better than the latter one in the more complex 2-source scenario. Finally, though the joint system suffers from a slight degradation in terms of classification accuracy with respect to the case where the source positions are known, it shows the advantage of carrying out the two tasks, recognition and localization, with a single system, and it allows the inclusion of information about the prior probabilities of the source positions. It is worth noticing also that, although the acoustic scenario used for experimentation is rather limited, the approach and its formalism were developed for a general case, where the number and identities of sources are not constrained

Binaural scene analysis : localization, detection and recognition of speakers in complex acoustic scenes

The human auditory system has the striking ability to robustly localize and recognize a specific target source in complex acoustic environments while ignoring interfering sources. Surprisingly, this remarkable capability, which is referred to as auditory scene analysis, is achieved by only analyzing the waveforms reaching the two ears. Computers, however, are presently not able to compete with the performance achieved by the human auditory system, even in the restricted paradigm of confronting a computer algorithm based on binaural signals with a highly constrained version of auditory scene analysis, such as localizing a sound source in a reverberant environment or recognizing a speaker in the presence of interfering noise. In particular, the problem of focusing on an individual speech source in the presence of competing speakers, termed the cocktail party problem, has been proven to be extremely challenging for computer algorithms. The primary objective of this thesis is the development of a binaural scene analyzer that is able to jointly localize, detect and recognize multiple speech sources in the presence of reverberation and interfering noise. The processing of the proposed system is divided into three main stages: localization stage, detection of speech sources, and recognition of speaker identities. The only information that is assumed to be known a priori is the number of target speech sources that are present in the acoustic mixture. Furthermore, the aim of this work is to reduce the performance gap between humans and machines by improving the performance of the individual building blocks of the binaural scene analyzer. First, a binaural front-end inspired by auditory processing is designed to robustly determine the azimuth of multiple, simultaneously active sound sources in the presence of reverberation. The localization model builds on the supervised learning of azimuthdependent binaural cues, namely interaural time and level differences. Multi-conditional training is performed to incorporate the uncertainty of these binaural cues resulting from reverberation and the presence of competing sound sources. Second, a speech detection module that exploits the distinct spectral characteristics of speech and noise signals is developed to automatically select azimuthal positions that are likely to correspond to speech sources. Due to the established link between the localization stage and the recognition stage, which is realized by the speech detection module, the proposed binaural scene analyzer is able to selectively focus on a predefined number of speech sources that are positioned at unknown spatial locations, while ignoring interfering noise sources emerging from other spatial directions. Third, the speaker identities of all detected speech sources are recognized in the final stage of the model. To reduce the impact of environmental noise on the speaker recognition performance, a missing data classifier is combined with the adaptation of speaker models using a universal background model. This combination is particularly beneficial in nonstationary background noise