Learning spectro-temporal representations of complex sounds with parameterized neural networks

Deep Learning models have become potential candidates for auditory neuroscience research, thanks to their recent successes on a variety of auditory tasks. Yet, these models often lack interpretability to fully understand the exact computations that have been performed. Here, we proposed a parametrized neural network layer, that computes specific spectro-temporal modulations based on Gabor kernels (Learnable STRFs) and that is fully interpretable. We evaluated predictive capabilities of this layer on Speech Activity Detection, Speaker Verification, Urban Sound Classification and Zebra Finch Call Type Classification. We found out that models based on Learnable STRFs are on par for all tasks with different toplines, and obtain the best performance for Speech Activity Detection. As this layer is fully interpretable, we used quantitative measures to describe the distribution of the learned spectro-temporal modulations. The filters adapted to each task and focused mostly on low temporal and spectral modulations. The analyses show that the filters learned on human speech have similar spectro-temporal parameters as the ones measured directly in the human auditory cortex. Finally, we observed that the tasks organized in a meaningful way: the human vocalizations tasks closer to each other and bird vocalizations far away from human vocalizations and urban sounds tasks

Computational Models of Representation and Plasticity in the Central Auditory System

The performance for automated speech processing tasks like speech recognition and speech activity detection rapidly degrades in challenging acoustic conditions. It is therefore necessary to engineer systems that extract meaningful information from sound while exhibiting invariance to background noise, different speakers, and other disruptive channel conditions. In this thesis, we take a biomimetic approach to these problems, and explore computational strategies used by the central auditory system that underlie neural information extraction from sound. In the first part of this thesis, we explore coding strategies employed by the central auditory system that yield neural responses that exhibit desirable noise robustness. We specifically demonstrate that a coding strategy based on sustained neural firings yields richly structured spectro-temporal receptive fields (STRFs) that reflect the structure and diversity of natural sounds. The emergent receptive fields are comparable to known physiological neuronal properties and can be employed as a signal processing strategy to improve noise invariance in a speech recognition task. Next, we extend the model of sound encoding based on spectro-temporal receptive fields to incorporate the cognitive effects of selective attention. We propose a framework for modeling attention-driven plasticity that induces changes to receptive fields driven by task demands. We define a discriminative cost function whose optimization and solution reflect a biologically plausible strategy for STRF adaptation that helps listeners better attend to target sounds. Importantly, the adaptation patterns predicted by the framework have a close correspondence with known neurophysiological data. We next generalize the framework to act on the spectro-temporal dynamics of task-relevant stimuli, and make predictions for tasks that have yet to be experimentally measured. We argue that our generalization represents a form of object-based attention, which helps shed light on the current debate about auditory attentional mechanisms. Finally, we show how attention-modulated STRFs form a high-fidelity representation of the attended target, and we apply our results to obtain improvements in a speech activity detection task. Overall, the results of this thesis improve our general understanding of central auditory processing, and our computational frameworks can be used to guide further studies in animal models. Furthermore, our models inspire signal processing strategies that are useful for automated speech and sound processing tasks

SENSORY AND PERCEPTUAL CODES IN CORTICAL AUDITORY PROCESSING

A key aspect of human auditory cognition is establishing efficient and reliable representations about the acoustic environment, especially at the level of auditory cortex. Since the inception of encoding models that relate sound to neural response, three longstanding questions remain open. First, on the apparently insurmountable problem of fundamental changes to cortical responses depending on certain categories of sound (e.g. simple tones versus environmental sound). Second, on how to integrate inner or subjective perceptual experiences into sound encoding models, given that they presuppose existing, direct physical stimulation which is sometimes missed. And third, on how does context and learning fine-tune these encoding rules, as adaptive changes to improve impoverished conditions particularly important for communication sounds. In this series, each question is addressed by analysis of mappings from sound stimuli delivered-to and/or perceived-by a listener, to large-scale cortically-sourced response time series from magnetoencephalography. It is first shown that the divergent, categorical modes of sensory coding may unify by exploring alternative acoustic representations other than the traditional spectrogram, such as temporal transient maps. Encoding models of either of artificial random tones, music, or speech stimulus classes, were substantially matched in their structure when represented from acoustic energy increases –consistent with the existence of a domain-general common baseline processing stage. Separately, the matter of the perceptual experience of sound via cortical responses is addressed via stereotyped rhythmic patterns normally entraining cortical responses with equal periodicity. Here, it is shown that under conditions of perceptual restoration, namely cases where a listener reports hearing a specific sound pattern in the midst of noise nonetheless, one may access such endogenous representations in the form of evoked cortical oscillations at the same rhythmic rate. Finally, with regards to natural speech, it is shown that extensive prior experience over repeated listening of the same sentence materials may facilitate the ability to reconstruct the original stimulus even where noise replaces it, and to also expedite normal cortical processing times in listeners. Overall, the findings demonstrate cases by which sensory and perceptual coding approaches jointly continue to expand the enquiry about listeners’ personal experience of the communication-rich soundscape

Neural Basis and Computational Strategies for Auditory Processing

Our senses are our window to the world, and hearing is the window through which we perceive the world of sound. While seemingly effortless, the process of hearing involves complex transformations by which the auditory system consolidates acoustic information from the environment into perceptual and cognitive experiences. Studies of auditory processing try to elucidate the mechanisms underlying the function of the auditory system, and infer computational strategies that are valuable both clinically and intellectually, hence contributing to our understanding of the function of the brain. In this thesis, we adopt both an experimental and computational approach in tackling various aspects of auditory processing. We first investigate the neural basis underlying the function of the auditory cortex, and explore the dynamics and computational mechanisms of cortical processing. Our findings offer physiological evidence for a role of primary cortical neurons in the integration of sound features at different time constants, and possibly in the formation of auditory objects. Based on physiological principles of sound processing, we explore computational implementations in tackling specific perceptual questions. We exploit our knowledge of the neural mechanisms of cortical auditory processing to formulate models addressing the problems of speech intelligibility and auditory scene analysis. The intelligibility model focuses on a computational approach for evaluating loss of intelligibility, inspired from mammalian physiology and human perception. It is based on a multi-resolution filter-bank implementation of cortical response patterns, which extends into a robust metric for assessing loss of intelligibility in communication channels and speech recordings. This same cortical representation is extended further to develop a computational scheme for auditory scene analysis. The model maps perceptual principles of auditory grouping and stream formation into a computational system that combines aspects of bottom-up, primitive sound processing with an internal representation of the world. It is based on a framework of unsupervised adaptive learning with Kalman estimation. The model is extremely valuable in exploring various aspects of sound organization in the brain, allowing us to gain interesting insight into the neural basis of auditory scene analysis, as well as practical implementations for sound separation in ``cocktail-party'' situations

Rhythmic auditory cortex activity at multiple timescales shapes stimulus–response gain and background firing

The phase of low-frequency network activity in the auditory cortex captures changes in neural excitability, entrains to the temporal structure of natural sounds, and correlates with the perceptual performance in acoustic tasks. Although these observations suggest a causal link between network rhythms and perception, it remains unknown how precisely they affect the processes by which neural populations encode sounds. We addressed this question by analyzing neural responses in the auditory cortex of anesthetized rats using stimulus–response models. These models included a parametric dependence on the phase of local field potential rhythms in both stimulus-unrelated background activity and the stimulus–response transfer function. We found that phase-dependent models better reproduced the observed responses than static models, during both stimulation with a series of natural sounds and epochs of silence. This was attributable to two factors: (1) phase-dependent variations in background firing (most prominent for delta; 1–4 Hz); and (2) modulations of response gain that rhythmically amplify and attenuate the responses at specific phases of the rhythm (prominent for frequencies between 2 and 12 Hz). These results provide a quantitative characterization of how slow auditory cortical rhythms shape sound encoding and suggest a differential contribution of network activity at different timescales. In addition, they highlight a putative mechanism that may implement the selective amplification of appropriately timed sound tokens relative to the phase of rhythmic auditory cortex activity

Dynamic and adaptive processing of speech in the human auditory cortex

Communicating through speech is an important part of everyday life, and losing that ability can be devastating. Millions of patients around the globe have lost the ability to hear or speak due to auditory cortex deficits. Doctor’s ability to help these patients has been hindered by a lack of understanding of the speech processing mechanisms in the human auditory cortex. This dissertation focuses on enhancing our understanding of the mechanisms of speech encoding in human primary and secondary auditory cortices using two methods of electroencephalography (EEG) and electrocorticography (ECoG). Phonemes are the smallest linguistic elements that can change a word’s meaning. I characterize EEG responses to continuous speech by obtaining the time-locked responses to phoneme instances (phoneme-related potential). I show that responses to different phoneme categories are organized by phonetic features, and each instance of a phoneme in continuous speech produces multiple distinguishable neural responses occurring as early as 50 ms and as late as 400 ms after the phoneme onset. Comparing the patterns of phoneme similarity in the neural responses and the acoustic signals confirms a repetitive appearance of acoustic distinctions of phonemes in the neural data. Analysis of the phonetic and speaker information in neural activations reveals that different time intervals jointly encode the acoustic similarity of both phonetic and speaker categories. These findings provide evidence for a dynamic neural transformation of low-level speech features as they propagate along the auditory pathway, and form an empirical framework to study the representational changes in learning, attention, and speech disorders. Later in this dissertation, I use ECoG neural recordings to explore mechanisms of speech communication in real-world environments that require adaptation to changing acoustic conditions. I explore how the human auditory cortex adapts as a new noise source appears in or disappears from the acoustic scene. To investigate the mechanisms of adaptation, neural activity in the auditory cortex of six human subjects were measured as they listened to speech with abruptly changing background noises. Rapid and selective suppression of acoustic features of noise in the neural responses are observed. This suppression results in enhanced representation and perception of speech acoustic features. The degree of adaptation to different background noises varies across neural sites and is predictable from the tuning properties and speech specificity of the sites. Moreover, adaptation to background noise is unaffected by the attentional focus of the listener. The convergence of these neural and perceptual effects reveals the intrinsic dynamic mechanisms that enable a listener to filter out irrelevant sound sources in a changing acoustic scene. Finally, in the last chapter, I introduce the Neural Acoustic Processing Library (NAPLib). NAPLib contains a suite of tools that characterize various properties of the neural representation of speech, which can be used for characterizing electrode tuning properties, and their response to phonemes. The library is applicable to both invasive and non-invasive recordings, including electroencephalography (EEG), electrocorticography (ECoG) and magnetoecnephalography (MEG). Together, this dissertation shows new evidence for dynamic and adaptive processing of speech sounds in the auditory pathway, and provides computational tools to study the dynamics of speech encoding in the human brain

Investigating the Neural Basis of Audiovisual Speech Perception with Intracranial Recordings in Humans

Speech is inherently multisensory, containing auditory information from the voice and visual information from the mouth movements of the talker. Hearing the voice is usually sufficient to understand speech, however in noisy environments or when audition is impaired due to aging or disabilities, seeing mouth movements greatly improves speech perception. Although behavioral studies have well established this perceptual benefit, it is still not clear how the brain processes visual information from mouth movements to improve speech perception. To clarify this issue, I studied the neural activity recorded from the brain surfaces of human subjects using intracranial electrodes, a technique known as electrocorticography (ECoG). First, I studied responses to noisy speech in the auditory cortex, specifically in the superior temporal gyrus (STG). Previous studies identified the anterior parts of the STG as unisensory, responding only to auditory stimulus. On the other hand, posterior parts of the STG are known to be multisensory, responding to both auditory and visual stimuli, which makes it a key region for audiovisual speech perception. I examined how these different parts of the STG respond to clear versus noisy speech. I found that noisy speech decreased the amplitude and increased the across-trial variability of the response in the anterior STG. However, possibly due to its multisensory composition, posterior STG was not as sensitive to auditory noise as the anterior STG and responded similarly to clear and noisy speech. I also found that these two response patterns in the STG were separated by a sharp boundary demarcated by the posterior-most portion of the Heschl’s gyrus. Second, I studied responses to silent speech in the visual cortex. Previous studies demonstrated that visual cortex shows response enhancement when the auditory component of speech is noisy or absent, however it was not clear which regions of the visual cortex specifically show this response enhancement and whether this response enhancement is a result of top-down modulation from a higher region. To test this, I first mapped the receptive fields of different regions in the visual cortex and then measured their responses to visual (silent) and audiovisual speech stimuli. I found that visual regions that have central receptive fields show greater response enhancement to visual speech, possibly because these regions receive more visual information from mouth movements. I found similar response enhancement to visual speech in frontal cortex, specifically in the inferior frontal gyrus, premotor and dorsolateral prefrontal cortices, which have been implicated in speech reading in previous studies. I showed that these frontal regions display strong functional connectivity with visual regions that have central receptive fields during speech perception

Computational and Perceptual Characterization of Auditory Attention

Humans are remarkably capable at making sense of a busy acoustic environment in real-time, despite the constant cacophony of sounds reaching our ears. Attention is a key component of the system that parses sensory input, allocating limited neural resources to elements with highest informational value to drive cognition and behavior. The focus of this thesis is the perceptual, neural, and computational characterization of auditory attention. Pioneering studies exploring human attention to natural scenes came from the visual domain, spawning a number of hypotheses on how attention operates among the visual pathway, as well as a considerable amount of computational work that attempt to model human perception. Comparatively, our understanding of auditory attention is yet very elementary, particularly pertaining to attention automatically drawn to salient sounds in the environment, such as a loud explosion. In this work, we explore how human perception is affected by the saliency of sound, characterized across a variety of acoustic features, such as pitch, loudness, and timbre. Insight from psychoacoustical data is complemented with neural measures of attention recorded directly from the brain using electroencephalography (EEG). A computational model of attention is presented, tracking the statistical regularities of incoming sound among a high-dimensional feature space to build predictions of future feature values. The model determines salient time points that will attract attention by comparing its predictions to the observed sound features. The high degree of agreement between the model and human experimental data suggests predictive coding as a potential mechanism of attention in the auditory pathway. We investigate different modes of volitional attention to natural acoustic scenes with a "cocktail-party" simulation. We argue that the auditory system can direct attention in at least three unique ways (globally, based on features, and based on objects) and that perception can be altered depending on how attention is deployed. Further, we illustrate how the saliency of sound affects the various modes of attention. The results of this work improve our understanding of auditory attention, highlighting the temporally evolving nature of sound as a significant distinction between audition and vision, with a focus on using natural scenes that engage the full capability of human attention