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

Speech localisation in a multitalker mixture by humans and machines

Speech localisation in multitalker mixtures is affected by the listener’s expectations about the spatial arrangement of the sound sources. This effect was investigated via experiments with human listeners and a machine system, in which the task was to localise a female-voice target among four spatially distributed male-voice maskers. Two configurations were used: either the masker locations were fixed or the locations varied from trial-to-trial. The machine system uses deep neural networks (DNNs) to learn the relationship between binaural cues and source azimuth, and exploits top-down knowledge about the spectral characteristics of the target source. Performance was examined in both anechoic and reverberant conditions. Our experiments show that the machine system outperformed listeners in some conditions. Both the machine and listeners were able to make use of a priori knowledge about the spatial configuration of the sources, but the effect for headphone listening was smaller than that previously reported for listening in a real room

A psychoacoustic engineering approach to machine sound source separation in reverberant environments

Reverberation continues to present a major problem for sound source separation algorithms, due to its corruption of many of the acoustical cues on which these algorithms rely. However, humans demonstrate a remarkable robustness to reverberation and many psychophysical and perceptual mechanisms are well documented. This thesis therefore considers the research question: can the reverberation–performance of existing psychoacoustic engineering approaches to machine source separation be improved? The precedence effect is a perceptual mechanism that aids our ability to localise sounds in reverberant environments. Despite this, relatively little work has been done on incorporating the precedence effect into automated sound source separation. Consequently, a study was conducted that compared several computational precedence models and their impact on the performance of a baseline separation algorithm. The algorithm included a precedence model, which was replaced with the other precedence models during the investigation. The models were tested using a novel metric in a range of reverberant rooms and with a range of other mixture parameters. The metric, termed Ideal Binary Mask Ratio, is shown to be robust to the effects of reverberation and facilitates meaningful and direct comparison between algorithms across different acoustic conditions. Large differences between the performances of the models were observed. The results showed that a separation algorithm incorporating a model based on interaural coherence produces the greatest performance gain over the baseline algorithm. The results from the study also indicated that it may be necessary to adapt the precedence model to the acoustic conditions in which the model is utilised. This effect is analogous to the perceptual Clifton effect, which is a dynamic component of the precedence effect that appears to adapt precedence to a given acoustic environment in order to maximise its effectiveness. However, no work has been carried out on adapting a precedence model to the acoustic conditions under test. Specifically, although the necessity for such a component has been suggested in the literature, neither its necessity nor benefit has been formally validated. Consequently, a further study was conducted in which parameters of each of the previously compared precedence models were varied in each room in order to identify if, and to what extent, the separation performance varied with these parameters. The results showed that the reverberation–performance of existing psychoacoustic engineering approaches to machine source separation can be improved and can yield significant gains in separation performance.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

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

Spatial auditory display for acoustics and music collections

PhDThis thesis explores how audio can be better incorporated into how people access information and does so by developing approaches for creating three-dimensional audio environments with low processing demands. This is done by investigating three research questions. Mobile applications have processor and memory requirements that restrict the number of concurrent static or moving sound sources that can be rendered with binaural audio. Is there a more e cient approach that is as perceptually accurate as the traditional method? This thesis concludes that virtual Ambisonics is an ef cient and accurate means to render a binaural auditory display consisting of noise signals placed on the horizontal plane without head tracking. Virtual Ambisonics is then more e cient than convolution of HRTFs if more than two sound sources are concurrently rendered or if movement of the sources or head tracking is implemented. Complex acoustics models require signi cant amounts of memory and processing. If the memory and processor loads for a model are too large for a particular device, that model cannot be interactive in real-time. What steps can be taken to allow a complex room model to be interactive by using less memory and decreasing the computational load? This thesis presents a new reverberation model based on hybrid reverberation which uses a collection of B-format IRs. A new metric for determining the mixing time of a room is developed and interpolation between early re ections is investigated. Though hybrid reverberation typically uses a recursive lter such as a FDN for the late reverberation, an average late reverberation tail is instead synthesised for convolution reverberation. Commercial interfaces for music search and discovery use little aural information even though the information being sought is audio. How can audio be used in interfaces for music search and discovery? This thesis looks at 20 interfaces and determines that several themes emerge from past interfaces. These include using a two or three-dimensional space to explore a music collection, allowing concurrent playback of multiple sources, and tools such as auras to control how much information is presented. A new interface, the amblr, is developed because virtual two-dimensional spaces populated by music have been a common approach, but not yet a perfected one. The amblr is also interpreted as an art installation which was visited by approximately 1000 people over 5 days. The installation maps the virtual space created by the amblr to a physical space

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

Acoustic localization of people in reverberant environments using deep learning techniques

La localización de las personas a partir de información acústica es cada vez más importante en aplicaciones del mundo real como la seguridad, la vigilancia y la interacción entre personas y robots. En muchos casos, es necesario localizar con precisión personas u objetos en función del sonido que generan, especialmente en entornos ruidosos y reverberantes en los que los métodos de localización tradicionales pueden fallar, o en escenarios en los que los métodos basados en análisis de vídeo no son factibles por no disponer de ese tipo de sensores o por la existencia de oclusiones relevantes. Por ejemplo, en seguridad y vigilancia, la capacidad de localizar con precisión una fuente de sonido puede ayudar a identificar posibles amenazas o intrusos. En entornos sanitarios, la localización acústica puede utilizarse para controlar los movimientos y actividades de los pacientes, especialmente los que tienen problemas de movilidad. En la interacción entre personas y robots, los robots equipados con capacidades de localización acústica pueden percibir y responder mejor a su entorno, lo que permite interacciones más naturales e intuitivas con los humanos. Por lo tanto, el desarrollo de sistemas de localización acústica precisos y robustos utilizando técnicas avanzadas como el aprendizaje profundo es de gran importancia práctica. Es por esto que en esta tesis doctoral se aborda dicho problema en tres líneas de investigación fundamentales: (i) El diseño de un sistema extremo a extremo (end-to-end) basado en redes neuronales capaz de mejorar las tasas de localización de sistemas ya existentes en el estado del arte. (ii) El diseño de un sistema capaz de localizar a uno o varios hablantes simultáneos en entornos con características y con geometrías de arrays de sensores diferentes sin necesidad de re-entrenar. (iii) El diseño de sistemas capaces de refinar los mapas de potencia acústica necesarios para localizar a las fuentes acústicas para conseguir una mejor localización posterior. A la hora de evaluar la consecución de dichos objetivos se han utilizado diversas bases de datos realistas con características diferentes, donde las personas involucradas en las escenas pueden actuar sin ningún tipo de restricción. Todos los sistemas propuestos han sido evaluados bajo las mismas condiciones consiguiendo superar en términos de error de localización a los sistemas actuales del estado del arte

Movements in Binaural Space: Issues in HRTF Interpolation and Reverberation, with applications to Computer Music

This thesis deals broadly with the topic of Binaural Audio. After reviewing the literature, a reappraisal of the minimum-phase plus linear delay model for HRTF representation and interpolation is offered. A rigorous analysis of threshold based phase unwrapping is also performed. The results and conclusions drawn from these analyses motivate the development of two novel methods for HRTF representation and interpolation. Empirical data is used directly in a Phase Truncation method. A Functional Model for phase is used in the second method based on the psychoacoustical nature of Interaural Time Differences. Both methods are validated; most significantly, both perform better than a minimum-phase method in subjective testing. The accurate, artefact-free dynamic source processing afforded by the above methods is harnessed in a binaural reverberation model, based on an early reflection image model and Feedback Delay Network diffuse field, with accurate interaural coherence. In turn, these flexible environmental processing algorithms are used in the development of a multi-channel binaural application, which allows the audition of multi-channel setups in headphones. Both source and listener are dynamic in this paradigm. A GUI is offered for intuitive use of the application. HRTF processing is thus re-evaluated and updated after a review of accepted practice. Novel solutions are presented and validated. Binaural reverberation is recognised as a crucial tool for convincing artificial spatialisation, and is developed on similar principles. Emphasis is placed on transparency of development practices, with the aim of wider dissemination and uptake of binaural technology