A Physiologically Inspired Method for Audio Classification

We explore the use of physiologically inspired auditory features with both physiologically motivated and statistical audio classification methods. We use features derived from a biophysically defensible model of the early auditory system for audio classification using a neural network classifier. We also use a Gaussian-mixture-model (GMM)-based classifier for the purpose of comparison and show that the neural-network-based approach works better. Further, we use features from a more advanced model of the auditory system and show that the features extracted from this model of the primary auditory cortex perform better than the features from the early auditory stage. The features give good classification performance with only one-second data segments used for training and testing

Physiologically Motivated Methods For Audio Pattern Classification

Human-like performance by machines in tasks of speech and audio processing has remained an elusive goal. In an attempt to bridge the gap in performance between humans and machines there has been an increased effort to study and model physiological processes. However, the widespread use of biologically inspired features proposed in the past has been hampered mainly by either the lack of robustness across a range of signal-to-noise ratios or the formidable computational costs. In physiological systems, sensor processing occurs in several stages. It is likely the case that signal features and biological processing techniques evolved together and are complementary or well matched. It is precisely for this reason that modeling the feature extraction processes should go hand in hand with modeling of the processes that use these features. This research presents a front-end feature extraction method for audio signals inspired by the human peripheral auditory system. New developments in the field of machine learning are leveraged to build classifiers to maximize the performance gains afforded by these features. The structure of the classification system is similar to what might be expected in physiological processing. Further, the feature extraction and classification algorithms can be efficiently implemented using the low-power cooperative analog-digital signal processing platform. The usefulness of the features is demonstrated for tasks of audio classification, speech versus non-speech discrimination, and speech recognition. The low-power nature of the classification system makes it ideal for use in applications such as hearing aids, hand-held devices, and surveillance through acoustic scene monitoringPh.D.Committee Chair: David V. Anderson; Committee Member: Chin-Hui Lee; Committee Member: James M. Rehg; Committee Member: Paul E. Hasler; Committee Member: Yucel Altunbasa

Low-Power Audio Classification For Ubiquitous Sensor Networks

In the past researchers have proposed a variety of features that are based on the human auditory system. However none of these features have been able to replace mel-frequency cepstral coefficients (MFCCs) as the preferred feature for audio classification problems, either because of computational costs involved or because of their poor performance in the presence of noise. In this paper we present new features derived from a model of the early auditory system. We compare the performance of the new features with MFCC in a four-class audio classification problem and show that they perform better. We also test the noise robustness of the new features in a two-way audio classification problem and show that it outperforms the MFCCs. Further, these new features can be implemented in low-power analog VLSI circuitry making them ideal for low-power sensor networks

Improving the noise-robustness of mel-frequency cepstral coefficients for speech processing,” in proc of

In this paper we study the noise-robustness of mel-frequency cepstral coefficients (MFCCs) and explore ways to improve their performance in noisy conditions. Improvements based on a more accurate model of the early auditory system are suggested to make the MFCC features more robust to noise while preserving their class discrimination ability. Speech versus non-speech classification and speech recognition are chosen to evaluate the performance gains afforded by the modifications. 1

DEDICATION

To my parents and to Parag, for their support, faith, and selfless love ACKNOWLEDGMENT First and foremost, I would like to thank my parents for everything they have done for me. My gratitude for their kindness and love cannot be expressed in words. My journey as a graduate student would not have begun without the constant encouragement and inspiration from my brother, Dr. Parag Ravindran. He has often been the calming influence during the frustrations of missed deadlines and failed experiments. I am forever indebted to him. I would like to express my deepest thanks to my thesis Advisor, Prof. David Anderson, for his guidance, patience, and support. His wonderful ability to balance guidance and exploratory learning has made this journey a valuable experience. I would also like to express my gratitude to my thesis committee members, Dr. Chin-Hui Lee, Dr. Paul Hasler, Dr. James Rehg and Dr. Yucel Altunbasak for their useful comments, suggestions, and readiness to help every time I approached them. I would like to express my heartfelt gratitude to Dr. Malcolm Slaney, for his guidance and advice. His ability to catch glitche

Clinical profile and diagnosis of tracheal bronchus among patients undergoing fiberoptic bronchoscopy in a tertiary level health facility

Background: Tracheobronchial anomalies are rare clinical entities and often asymptomatic in nature. Some patients may experience symptoms such as cough, recurrent pneumonia, or hemoptysis. Tracheal bronchus is one of the rarer forms of tracheobronchial anomalies, which may be seen during routine bronchoscopy. Knowledge and understanding of tracheal bronchus is important for diagnosing symptomatic patients and performing certain procedures, including bronchoscopy and endotracheal intubation. Objective: The objective is to study the clinical profile, diagnosis, and management of tracheal bronchus detected during routine bronchoscopy in a tertiary care setting. Methods: This study was a retrospective analysis of hospital data of patients undergoing fiberoptic bronchoscopy for 2 years in a tertiary care setting. Results: There were 150 bronchoscopies performed during the period. A total of 42 anomalies were detected in 35 (23.33%) patients. Three patients had tracheal bronchus (2%). Conclusions: This retrospective study evaluated the presence of tracheal bronchus among patients who underwent bronchoscopy in a tertiary care hospital in Kerala, India. This study revealed that tracheal bronchus was present in 2% of all bronchoscopies done during that period

Towards Low-Power On-chip Auditory Processing

Machine perception is a difficult problem both from a practical or implementation point of view as well as from a theoretical or algorithm point of view. Machine perception systems based on biological perception systems show great promise in many areas but they often have processing requirements and/or data flow requirements that are difficult to implement, especially in small or low-power systems. We propose a system design approach that makes it possible to implement complex functionality using cooperative analog-digital signal processing to lower-power requirements dramatically over digital-only systems, as well as provide an architecture facilitating the development of biologically motivated perception systems. We show the architecture and application development approach. We also present several reference systems for speech recognition, noise suppression, and audio classification.</p