63 research outputs found

    Data-Driven Audiogram Classification for Mobile Audiometry

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    Recent mobile and automated audiometry technologies have allowed for the democratization of hearing healthcare and enables non-experts to deliver hearing tests. The problem remains that a large number of such users are not trained to interpret audiograms. In this work, we outline the development of a data-driven audiogram classification system designed specifically for the purpose of concisely describing audiograms. More specifically, we present how a training dataset was assembled and the development of the classification system leveraging supervised learning techniques. We show that three practicing audiologists had high intra- and inter-rater agreement over audiogram classification tasks pertaining to audiogram configuration, symmetry and severity. The system proposed here achieves a performance comparable to the state of the art, but is signific

    Neuromorphic audio processing through real-time embedded spiking neural networks.

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    In this work novel speech recognition and audio processing systems based on a spiking artificial cochlea and neural networks are proposed and implemented. First, the biological behavior of the animal’s auditory system is analyzed and studied, along with the classical mechanisms of audio signal processing for sound classification, including Deep Learning techniques. Based on these studies, novel audio processing and automatic audio signal recognition systems are proposed, using a bio-inspired auditory sensor as input. A desktop software tool called NAVIS (Neuromorphic Auditory VIsualizer) for post-processing the information obtained from spiking cochleae was implemented, allowing to analyze these data for further research. Next, using a 4-chip SpiNNaker hardware platform and Spiking Neural Networks, a system is proposed for classifying different time-independent audio signals, making use of a Neuromorphic Auditory Sensor and frequency studies obtained with NAVIS. To prove the robustness and analyze the limitations of the system, the input audios were disturbed, simulating extreme noisy environments. Deep Learning mechanisms, particularly Convolutional Neural Networks, are trained and used to differentiate between healthy persons and pathological patients by detecting murmurs from heart recordings after integrating the spike information from the signals using a neuromorphic auditory sensor. Finally, a similar approach is used to train Spiking Convolutional Neural Networks for speech recognition tasks. A novel SCNN architecture for timedependent signals classification is proposed, using a buffered layer that adapts the information from a real-time input domain to a static domain. The system was deployed on a 48-chip SpiNNaker platform. Finally, the performance and efficiency of these systems were evaluated, obtaining conclusions and proposing improvements for future works.Premio Extraordinario de Doctorado U

    THE USE OF REGRESSION MODELS FOR DETECTING DIGITAL FINGERPRINTS IN SYNTHETIC AUDIO

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    Modern advancements in text to speech and voice conversion techniques make it increasingly difficult to distinguish an authentic voice from a synthetically generated voice. These techniques, though complex, are relatively easy to use, even for non-technical users. It is important to develop mechanisms for detecting false content that easily scale to the size of the monitoring requirement. Current approaches for detecting spoofed audio are difficult to scale because of their processing requirements. Individually analyzing spectrograms for aberrations at higher frequencies relies too much on independent verification and is more resource intensive. Our method addresses the resource consideration by only looking at the residual differences between an audio file’s smoothed signal and its actual signal. We conjecture that natural audio has greater variance than spoofed audio because spoofed audio’s generation is conditioned on trying to mimic an existing pattern. To test this, we develop a classifier that distinguishes between spoofed and real audio by analyzing the differences in residual patterns between audio files.Outstanding ThesisMajor, United States ArmyApproved for public release. Distribution is unlimited

    Determining the impact of neocortex size changes on information processing : implications for neurodevelopmental disorders

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    Development of the cerebral cortex is highly regulated by genetically programmed sequential events. Alterations in this process by genetic or non-genetic factors can lead to over- or underproduction of cortical neurons and abnormal connectivity, resulting in neurodevelopmental disorders, such as autism spectrum disorder (ASD). Although ASD patients often exhibit atypical sensory processing, the relationship between abnormal cortical size and sensory processing is still unclear. Here we hypothesise that abnormal cortical expansion during development leads to atypical sensory processing. To test this hypothesis, we utilized a recently established mouse model, in which the number of superficial cortical excitatory neurons is increased pharmacologically during embryonic development. This mouse model is also known to exhibit macrocephaly and autism-like phenotypes. In this project, we assessed their auditory perception behaviourally and evaluated neuronal population activity in the auditory cortex and medial geniculate nucleus through high-density in vivo electrophysiological recording. Our results indicate that the overproduction of superficial cortical excitatory neurons negatively affects auditory processing. Treated mice exhibited hyposensitivity to near-threshold auditory stimuli during behavioural auditory detection assessment. Further, their cortical neurons showed lower spontaneous and auditory evoked activity as well as delayed peak response latency. We also observed atypical cortical functional connectivity. Overall, our study suggests that abnormal cortical expansion during development results in atypical auditory processing, and provides further insights into the neural basis of perceptual deficits in neurodevelopmental disorders such as ASD.Development of the cerebral cortex is highly regulated by genetically programmed sequential events. Alterations in this process by genetic or non-genetic factors can lead to over- or underproduction of cortical neurons and abnormal connectivity, resulting in neurodevelopmental disorders, such as autism spectrum disorder (ASD). Although ASD patients often exhibit atypical sensory processing, the relationship between abnormal cortical size and sensory processing is still unclear. Here we hypothesise that abnormal cortical expansion during development leads to atypical sensory processing. To test this hypothesis, we utilized a recently established mouse model, in which the number of superficial cortical excitatory neurons is increased pharmacologically during embryonic development. This mouse model is also known to exhibit macrocephaly and autism-like phenotypes. In this project, we assessed their auditory perception behaviourally and evaluated neuronal population activity in the auditory cortex and medial geniculate nucleus through high-density in vivo electrophysiological recording. Our results indicate that the overproduction of superficial cortical excitatory neurons negatively affects auditory processing. Treated mice exhibited hyposensitivity to near-threshold auditory stimuli during behavioural auditory detection assessment. Further, their cortical neurons showed lower spontaneous and auditory evoked activity as well as delayed peak response latency. We also observed atypical cortical functional connectivity. Overall, our study suggests that abnormal cortical expansion during development results in atypical auditory processing, and provides further insights into the neural basis of perceptual deficits in neurodevelopmental disorders such as ASD

    Predicting room acoustical behavior with the ODEON computer model

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    Advances in Neural Signal Processing

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    Neural signal processing is a specialized area of signal processing aimed at extracting information or decoding intent from neural signals recorded from the central or peripheral nervous system. This has significant applications in the areas of neuroscience and neural engineering. These applications are famously known in the area of brain–machine interfaces. This book presents recent advances in this flourishing field of neural signal processing with demonstrative applications

    Advances in Neural Signal Processing

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    Neural signal processing is a specialized area of signal processing aimed at extracting information or decoding intent from neural signals recorded from the central or peripheral nervous system. This has significant applications in the areas of neuroscience and neural engineering. These applications are famously known in the area of brain–machine interfaces. This book presents recent advances in this flourishing field of neural signal processing with demonstrative applications

    Treatise on Hearing: The Temporal Auditory Imaging Theory Inspired by Optics and Communication

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    A new theory of mammalian hearing is presented, which accounts for the auditory image in the midbrain (inferior colliculus) of objects in the acoustical environment of the listener. It is shown that the ear is a temporal imaging system that comprises three transformations of the envelope functions: cochlear group-delay dispersion, cochlear time lensing, and neural group-delay dispersion. These elements are analogous to the optical transformations in vision of diffraction between the object and the eye, spatial lensing by the lens, and second diffraction between the lens and the retina. Unlike the eye, it is established that the human auditory system is naturally defocused, so that coherent stimuli do not react to the defocus, whereas completely incoherent stimuli are impacted by it and may be blurred by design. It is argued that the auditory system can use this differential focusing to enhance or degrade the images of real-world acoustical objects that are partially coherent. The theory is founded on coherence and temporal imaging theories that were adopted from optics. In addition to the imaging transformations, the corresponding inverse-domain modulation transfer functions are derived and interpreted with consideration to the nonuniform neural sampling operation of the auditory nerve. These ideas are used to rigorously initiate the concepts of sharpness and blur in auditory imaging, auditory aberrations, and auditory depth of field. In parallel, ideas from communication theory are used to show that the organ of Corti functions as a multichannel phase-locked loop (PLL) that constitutes the point of entry for auditory phase locking and hence conserves the signal coherence. It provides an anchor for a dual coherent and noncoherent auditory detection in the auditory brain that culminates in auditory accommodation. Implications on hearing impairments are discussed as well.Comment: 603 pages, 131 figures, 13 tables, 1570 reference

    Advances in Neural Signal Processing

    Get PDF
    Neural signal processing is a specialized area of signal processing aimed at extracting information or decoding intent from neural signals recorded from the central or peripheral nervous system. This has significant applications in the areas of neuroscience and neural engineering. These applications are famously known in the area of brain–machine interfaces. This book presents recent advances in this flourishing field of neural signal processing with demonstrative applications
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