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

Survival Following Kidney Sparing Management of Upper Urinary Tract Transitional Cell Carcinoma is Adversely Affected By Prior History of Bladder Cancer

Objective: To evaluate oncological outcomes of Kidney Sparing Surgery (KSS) for upper urinary tract transitional cell carcinoma (UUT-TCC). Patients and Methods: We performed a retrospective review of patients who underwent segmental ureterectomy or endoscopic treatment (percutaneous nephroscopy or retrograde ureteroscopy) for UUT-TCC between 1991 and 2006 at our institution. We evaluated recurrence-free and overall survival rates following KSS. There were 40 renal units in 38 patients. Three patients had bilateral synchronous disease. Mean patient age (±SD) was 69.8±12.3 years. Eighteen (47%) patients had a prior history of bladder TCC. Sixteen (40%) segmental ureterectomies and 24 (60%) endoscopic treatments were performed. Six (16%) patients received adjuvant BCG. Grade distribution was 24 (60%) low-grade, 12 (30%) high-grade and 4 (10%) Gx. The mean follow-up was 47 months. Results: Recurrence rate was 32.5%. The three and five-year recurrence-free survivals were 59.5% and 42.4%. Tumor location was predictive for recurrence (p <0.03). The three and five-year overall survivals were 91.6% and 79.8%. Predictive variables for overall survival were tumor grade (p <0.008) and stage (p <0.018) and previous history of bladder TCC. There was a statistically significant correlation (r= 0.3539) between tumor grade and stage (p= 0.027). Conclusions: KSS offers good oncological outcomes in selected patients with UUT-TCC. The tumor biology rather than the surgical approach dictates prognosis. Patients with higher stage and grade disease may be better served with a more aggressive treatment approach.Key Words: Upper Urinary Tract, Transitional Cell Carcinoma, Endoscopic, Kidney Sparing Surgery, Ureteroscopy, Percutaneous, Survival, Recurrenc

DPM-TSE: A Diffusion Probabilistic Model for Target Sound Extraction

Common target sound extraction (TSE) approaches primarily relied on discriminative approaches in order to separate the target sound while minimizing interference from the unwanted sources, with varying success in separating the target from the background. This study introduces DPM-TSE, a first generative method based on diffusion probabilistic modeling (DPM) for target sound extraction, to achieve both cleaner target renderings as well as improved separability from unwanted sounds. The technique also tackles common background noise issues with DPM by introducing a correction method for noise schedules and sample steps. This approach is evaluated using both objective and subjective quality metrics on the FSD Kaggle 2018 dataset. The results show that DPM-TSE has a significant improvement in perceived quality in terms of target extraction and purity.Comment: Submitted to ICASSP 202

Are acoustics enough? Semantic effects on auditory salience in natural scenes

Auditory salience is a fundamental property of a sound that allows it to grab a listener's attention regardless of their attentional state or behavioral goals. While previous research has shed light on acoustic factors influencing auditory salience, the semantic dimensions of this phenomenon have remained relatively unexplored owing both to the complexity of measuring salience in audition as well as limited focus on complex natural scenes. In this study, we examine the relationship between acoustic, contextual, and semantic attributes and their impact on the auditory salience of natural audio scenes using a dichotic listening paradigm. The experiments present acoustic scenes in forward and backward directions; the latter allows to diminish semantic effects, providing a counterpoint to the effects observed in forward scenes. The behavioral data collected from a crowd-sourced platform reveal a striking convergence in temporal salience maps for certain sound events, while marked disparities emerge in others. Our main hypothesis posits that differences in the perceptual salience of events are predominantly driven by semantic and contextual cues, particularly evident in those cases displaying substantial disparities between forward and backward presentations. Conversely, events exhibiting a high degree of alignment can largely be attributed to low-level acoustic attributes. To evaluate this hypothesis, we employ analytical techniques that combine rich low-level mappings from acoustic profiles with high-level embeddings extracted from a deep neural network. This integrated approach captures both acoustic and semantic attributes of acoustic scenes along with their temporal trajectories. The results demonstrate that perceptual salience is a careful interplay between low-level and high-level attributes that shapes which moments stand out in a natural soundscape. Furthermore, our findings underscore the important role of longer-term context as a critical component of auditory salience, enabling us to discern and adapt to temporal regularities within an acoustic scene. The experimental and model-based validation of semantic factors of salience paves the way for a complete understanding of auditory salience. Ultimately, the empirical and computational analyses have implications for developing large-scale models for auditory salience and audio analytics

Connecting Deep Neural Networks to Physical, Perceptual, and Electrophysiological Auditory Signals

Deep neural networks have been recently shown to capture intricate information transformation of signals from the sensory profiles to semantic representations that facilitate recognition or discrimination of complex stimuli. In this vein, convolutional neural networks (CNNs) have been used very successfully in image and audio classification. Designed to imitate the hierarchical structure of the nervous system, CNNs reflect activation with increasing degrees of complexity that transform the incoming signal onto object-level representations. In this work, we employ a CNN trained for large-scale audio object classification to gain insights about the contribution of various audio representations that guide sound perception. The analysis contrasts activation of different layers of a CNN with acoustic features extracted directly from the scenes, perceptual salience obtained from behavioral responses of human listeners, as well as neural oscillations recorded by electroencephalography (EEG) in response to the same natural scenes. All three measures are tightly linked quantities believed to guide percepts of salience and object formation when listening to complex scenes. The results paint a picture of the intricate interplay between low-level and object-level representations in guiding auditory salience that is very much dependent on context and sound category