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

The Use of Optimal Cue Mapping to Improve the Intelligibility and Quality of Speech in Complex Binaural Sound Mixtures.

A person with normal hearing has the ability to follow a particular conversation of interest in a noisy and reverberant environment, whilst simultaneously ignoring the interfering sounds. This task often becomes more challenging for individuals with a hearing impairment. Attending selectively to a sound source is difficult to replicate in machines, including devices such as hearing aids. A correctly set up hearing aid will work well in quiet conditions, but its performance may deteriorate seriously in the presence of competing sounds. To be of help in these more challenging situations the hearing aid should be able to segregate the desired sound source from any other, unwanted sounds. This thesis explores a novel approach to speech segregation based on optimal cue mapping (OCM). OCM is a signal processing method for segregating a sound source based on spatial and other cues extracted from the binaural mixture of sounds arriving at a listener's ears. The spectral energy fraction of the target speech source in the mixture is estimated frame-by-frame using artificial neural networks (ANNs). The resulting target speech magnitude estimates for the left and right channels are combined with the corresponding original phase spectra to produce the final binaural output signal. The performance improvements delivered by the OCM algorithm are evaluated using the STOI and PESQ metrics for speech intelligibility and quality, respectively. A variety of increasingly challenging binaural mixtures are synthesised involving up to five spatially separate sound sources in both anechoic and reverberant environments. The segregated speech consistently exhibits gains in intelligibility and quality and compares favourably with a leading, somewhat more complex approach. The OCM method allows the selection and integration of multiple cues to be optimised and provides scalable performance benefits to suit the available computational resources. The ability to determine the varying relative importance of each cue in different acoustic conditions is expected to facilitate computationally efficient solutions suitable for use in a hearing aid, allowing the aid to operate effectively in a range of typical acoustic environments. Further developments are proposed to achieve this overall goal

Studies on auditory processing of spatial sound and speech by neuromagnetic measurements and computational modeling

This thesis addresses the auditory processing of spatial sound and speech. The thesis consists of two research branches: one, magnetoencephalographic (MEG) brain measurements on spatial localization and speech perception, and two, construction of computational auditory scene analysis models, which exploit spatial cues and other cues that are robust in reverberant environments. In the MEG research branch, we have addressed the processing of the spatial stimuli in the auditory cortex through studies concentrating to the following issues: processing of sound source location with realistic spatial stimuli, spatial processing of speech vs. non-speech stimuli, and finally processing of range of spatial location cues in the auditory cortex. Our main findings are as follows: Both auditory cortices respond more vigorously to contralaterally presented sound, whereby responses exhibit systematic tuning to the sound source direction. Responses and response dynamics are generally larger in the right hemisphere, which indicates right hemispheric specialization in the spatial processing. These observations hold over the range of speech and non-speech stimuli. The responses to speech sounds are decreased markedly if the natural periodic speech excitation is changed to random noise sequence. Moreover, the activation strength of the right auditory cortex seems to reflect processing of spatial cues, so that the dynamical differences are larger and the angular organization is more orderly for realistic spatial stimuli compared to impoverished spatial stimuli (e.g. isolated interaural time and level difference cues). In the auditory modeling part, we constructed models for the recognition of speech in the presence of interference. Firstly, we constructed a system using binaural cues in order to segregate target speech from spatially separated interference, and showed that the system outperforms a conventional approach at low signal-to-noise ratios. Secondly, we constructed a single channel system that is robust in room reverberation using strong speech modulations as robust cues, and showed that it outperforms a baseline approach in the most reverberant test conditions. In this case, the baseline approach was specifically optimized for recognition of speech in reverberation. In summary, this thesis addresses the auditory processing of spatial sound and speech in both brain measurement and auditory modeling. The studies aim to clarify cortical processes of sound localization, and to construct computational auditory models for sound segregation exploiting spatial cues, and strong speech modulations as robust cues in reverberation.reviewe

Informed algorithms for sound source separation in enclosed reverberant environments

While humans can separate a sound of interest amidst a cacophony of contending sounds in an echoic environment, machine-based methods lag behind in solving this task. This thesis thus aims at improving performance of audio separation algorithms when they are informed i.e. have access to source location information. These locations are assumed to be known a priori in this work, for example by video processing. Initially, a multi-microphone array based method combined with binary time-frequency masking is proposed. A robust least squares frequency invariant data independent beamformer designed with the location information is utilized to estimate the sources. To further enhance the estimated sources, binary time-frequency masking based post-processing is used but cepstral domain smoothing is required to mitigate musical noise. To tackle the under-determined case and further improve separation performance at higher reverberation times, a two-microphone based method which is inspired by human auditory processing and generates soft time-frequency masks is described. In this approach interaural level difference, interaural phase difference and mixing vectors are probabilistically modeled in the time-frequency domain and the model parameters are learned through the expectation-maximization (EM) algorithm. A direction vector is estimated for each source, using the location information, which is used as the mean parameter of the mixing vector model. Soft time-frequency masks are used to reconstruct the sources. A spatial covariance model is then integrated into the probabilistic model framework that encodes the spatial characteristics of the enclosure and further improves the separation performance in challenging scenarios i.e. when sources are in close proximity and when the level of reverberation is high. Finally, new dereverberation based pre-processing is proposed based on the cascade of three dereverberation stages where each enhances the twomicrophone reverberant mixture. The dereverberation stages are based on amplitude spectral subtraction, where the late reverberation is estimated and suppressed. The combination of such dereverberation based pre-processing and use of soft mask separation yields the best separation performance. All methods are evaluated with real and synthetic mixtures formed for example from speech signals from the TIMIT database and measured room impulse responses

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

Effect of Reverberation Context on Spatial Hearing Performance of Normally Hearing Listeners

Previous studies provide evidence that listening experience in a particular reverberant environment improves speech intelligibility and localization performance in that environment. Such studies, however, are few, and there is little knowledge of the underlying mechanisms. The experiments presented in this thesis explored the effect of reverberation context, in particular, the similarity in interaural coherence within a context, on listeners\u27 performance in sound localization, speech perception in a spatially separated noise, spatial release from speech-on-speech masking, and target location identification in a multi-talker configuration. All experiments were conducted in simulated reverberant environments created with a loudspeaker array in an anechoic chamber. The reflections comprising the reverberation in each environment had the same temporal and relative amplitude patterns, but varied in their lateral spread, which affected the interaural coherence of reverberated stimuli. The effect of reverberation context was examined by comparing performance in two reverberation contexts, mixed and fixed. In the mixed context, the reverberation environment applied to each stimulus varied trial-by-trial, whereas in the fixed context, the reverberation environment was held constant within a block of trials. In Experiment I (absolute judgement of sound location), variability in azimuth judgments was lower in the fixed than in the mixed context, suggesting that sound localization depended not only on the cues presented in isolated trials. In Experiment II, the intelligibility of speech in a spatially separated noise was found to be similar in both reverberation contexts. That result contrasts with other studies, and suggests that the fixed context did not assist listeners in compensating for degraded interaural coherence. In Experiment III, speech intelligibility in multi-talker configurations was found to be better in the fixed context, but only when the talkers were separated. That is, the fixed context improved spatial release from masking. However, in the presence of speech maskers, consistent reverberation did not improve the localizability of the target talker in a three-alternative location-identification task. Those results suggest that in multi-talker situations, consistent coherence may not improve target localizability, but rather that consistent context may facilitate the buildup of spatial selective attention

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

Adaptive time-frequency analysis for cognitive source separation

This thesis introduces a framework for separating two speech sources in non-ideal, reverberant environments. The source separation architecture tries to mimic the extraordinary abilities of the human auditory system when performing source separation. A movable human dummy head residing in a normal office room is used to model the conditions humans experience when listening to complex auditory scenes. This thesis first investigates how the orthogonality of speech sources in the time-frequency domain drops with different reverberation times of the environment and shows that separation schemes based on ideal binary time-frequency-masks are suitable to perform source separation also under humanoid reverberant conditions. Prior to separating the sources, the movable human dummy head analyzes the auditory scene and estimates the positions of the sources and the fundamental frequency tracks. The source localization is implemented using an iterative approach based on the interaural time differences between the two ears and achieves a localization blur of less than three degrees in the azimuth plane. The source separation architecture implemented in this thesis extracts the orthogonal timefrequency points of the speech mixtures. It combines the positive features of the STFT with the positive features of the cochleagram representation. The overall goal of the source separation is to find the ideal STFT-mask. The core source separation process however is based on the analysis of the corresponding region in an additionally computed cochleagram, which shows more reliable Interaural Time Difference (ITD) estimations that are used for separation. Several algorithms based on the ITD and the fundamental frequency of the target source are evaluated for their source separation capabilities. To enhance the separation capabilities of the single algorithms, the results of the different algorithms are combined to compute a final estimate. In this way SIR gains of approximately 30 dB for two source scenarios are achieved. For three source scenarios SIR gains of up to 16 dB are attained. Compared to the standard binaural signal processing approaches like DUET and Fixed Beamforming the presented approach achieves up to 29 dB SIR gain.Diese Dissertation beschreibt ein Framework zur Separation zweier Quellen in nicht-idealen, echobehafteten Umgebungen. Die Architektur zur Quellenseparation orientiert sich dabei an den außergewöhnlichen Separationsfähigkeiten des menschlichen Gehörs. Um die Bedingungen eines Menschen in einer komplexen auditiven Szene zu imitieren, wird ein beweglicher, menschlicher Kunstkopf genutzt, der sich in einem üblichen Büroraum befindet. In einem ersten Schritt analysiert diese Dissertation, inwiefern die Orthogonalität von Sprachsignalen im Zeit-Frequenz-Bereich mit unterschiedlichen Nachhallzeiten abnimmt. Trotz der Orthogonalitätsabnahme sind Separationsansätze basierend auf idealen binären Masken geeignet um eine Trennung von Sprachsignalen auch unter menschlichen, echobehafteten Bedingungen zu realisieren. Bevor die Quellen getrennt werden, analysiert der bewegliche Kunstkopf die auditive Szene und schätzt die Positionen der einzelnen Quellen und den Verlauf der Grundfrequenz der Sprecher ab. Die Quellenlokalisation wird durch einen iterativen Ansatz basierend auf den Zeitunterschieden zwischen beiden Ohren verwirklicht und erreicht eine Lokalisierungsgenauigkeit von weniger als drei Grad in der Azimuth-Ebene. Die Quellenseparationsarchitektur die in dieser Arbeit implementiert wird, extrahiert die orthogonalen Zeit-Frequenz-Punkte der Sprachmixturen. Dazu werden die positiven Eigenschaften der STFT mit den positiven Eigenschaften des Cochleagrams kombiniert. Ziel ist es, die ideale STFT-Maske zu finden. Die eigentliche Quellentrennung basiert jedoch auf der Analyse der entsprechenden Region eines zusätzlich berechneten Cochleagrams. Auf diese Weise wird eine weitaus verlässlichere Auswertung der Zeitunterschiede zwischen den beiden Ohren verwirklicht. Mehrere Algorithmen basierend auf den interauralen Zeitunterschieden und der Grundfrequenz der Zielquelle werden bezüglich ihrer Separationsfähigkeiten evaluiert. Um die Trennungsmöglichkeiten der einzelnen Algorithmen zu erhöhen, werden die einzelnen Ergebnisse miteinander verknüpft um eine finale Abschätzung zu gewinnen. Auf diese Weise können SIR Gewinne von ungefähr 30 dB für Szenarien mit zwei Quellen erzielt werden. Für Szenarien mit drei Quellen werden Gewinne von bis zu 16 dB erzielt. Verglichen mit binauralen Standardverfahren zur Quellentrennung wie DUET oder Fixed Beamforming, gewinnt der vorgestellte Ansatz bis zu 29 dB SIR

Physiology-based model of multi-source auditory processing

Our auditory systems are evolved to process a myriad of acoustic environments. In complex listening scenarios, we can tune our attention to one sound source (e.g., a conversation partner), while monitoring the entire acoustic space for cues we might be interested in (e.g., our names being called, or the fire alarm going off). While normal hearing listeners handle complex listening scenarios remarkably well, hearing-impaired listeners experience difficulty even when wearing hearing-assist devices. This thesis presents both theoretical work in understanding the neural mechanisms behind this process, as well as the application of neural models to segregate mixed sources and potentially help the hearing impaired population. On the theoretical side, auditory spatial processing has been studied primarily up to the midbrain region, and studies have shown how individual neurons can localize sounds using spatial cues. Yet, how higher brain regions such as the cortex use this information to process multiple sounds in competition is not clear. This thesis demonstrates a physiology-based spiking neural network model, which provides a mechanism illustrating how the auditory cortex may organize up-stream spatial information when there are multiple competing sound sources in space. Based on this model, an engineering solution to help hearing-impaired listeners segregate mixed auditory inputs is proposed. Using the neural model to perform sound-segregation in the neural domain, the neural outputs (representing the source of interest) are reconstructed back to the acoustic domain using a novel stimulus reconstruction method.2017-09-22T00:00:00