COMSOL Cochlear Mechanics — Biomedical Instrumentation & Clinical Sensing

Abstract — COMSOL Cochlear Mechanics

stacks, simulation worlds, reports and viva from Bangalore. DRAFT – MICROPHONE ARRAY SIGNAL PROCESSING AND DEEP LEARNING FOR SPEECH ENHANCEMENT Reinhold Haeb-Umbach∗, Tomohiro Nakatani†, Marc Delcroix†, Christoph Boeddeker∗, Figure 1: Diagnostic Medical Waveform & Bio-Potential Clinical Trace for Comsol Cochlear Mechanics Multi-channel acoustic signal processing is a well-established and powerful tool to exploit the spatial diversity between a target signal and non-target or noise sources for signal enhancement. However, the textbook solutions for optimal data-dependent spatial filtering rest on the knowledge of second-order statistical moments of the signals, which have traditionally been difficult to acquire. In this contribution, we compare model-based, purely data-driven, and hybrid approaches to parameter estimation and filtering, where the latter tries to combine the benefits of model-based signal processing and data-driven deep learning to overcome their individual deficiencies. We illustrate the underlying design principles with examples from noise reduction, source separation, and dereverberation.

Sensor Interfacing & Analog Front-End Conditioning

Microphone array processing, speech enhancement, deep learning, source separation Speech enhancement has been a research topic for many years. It is concerned with removing dis- tortions, such as ambient noise, from a degraded speech signal. Its various applications fall into two classes: improving human-to-human communication and improving human-to-machine communication, i.e., automatic speech recognition (ASR). In the first case, where the human is the consumer of the enhanced signal, the focus is on enhancing the quality, and ideally also the intelligibility, of the signal captured by the microphone(s). Examples of applications are telecommunications, hearing aids, media archive enhancement, etc. –. If the enhancement stage is followed by an ASR system, the goal is to improve the recognition accuracy of the recognizer.

Bio-Signal Waveform Analysis & Simulation Findings

Figure 2: Diagnostic Medical Waveform & Bio-Potential Clinical Trace for Comsol Cochlear Mechanics DRAFT – MICROPHONE ARRAY SIGNAL PROCESSING AND DEEP LEARNING FOR SPEECH ENHANCEMENT There are many ways in which a speech signal can be distorted. It can be degraded by the superpo- sition of other acoustic signals, such as by environmental noise or competing speakers. The multi-path propagation from the speech source to the microphones, known as reverberation, leads to degraded intelligibility and ASR performance. And there are many other distortions, e.g., line or acoustic echoes, signal clipping, etc. Consequently, the field of speech enhancement is large, and a plethora of techniques have been proposed to clean up a speech signal.

Scope & Clinical Applications

• Real-time multi-parameter vital signs telemetry (ECG, Heart Rate, SpO2, Temperature, Respiration)
• Low-noise Analog Front-End (AFE) conditioning with active Driven Right Leg (DRL) 50 Hz cancellation
• Digital signal processing in MATLAB: Pan-Tompkins QRS detection, FIR/IIR notch, and Continuous Wavelet Transforms
• Finite element tissue bio-electromagnetics and orthopedic mechanical simulations in COMSOL & ANSYS
• Complete Arduino firmware .INO sketches, circuit schematics, and university dissertation reports