Carnegie Mellon University

Representation of a soundwave, white, grey and pink on a pink background

Channel Profiling

By Shinji Watanabe

This project aims to develop a solution capable of accurately analyzing and quantifying multiple speech and acoustic environment characteristics found in audio inputs. Our objective is to provide a comprehensive understanding of audio clips, including the assessment of reverberation levels, noise types and levels, transmission interference, and other relevant factors. Furthermore, we intend to incorporate other speech and speaker-based attributes such as speech rate, accent, and language into the analysis. Ultimately, the proposed solution will generate quantifiable metrics summarizing the identified characteristics. To achieve our goal, we propose the development of an advanced system for classifying and estimating acoustic environment characteristics based on deep learning conditioned on various acoustic/linguistic cues, including a reference signal in a target environment. This system will leverage cutting-edge techniques and algorithms in audio processing, machine learning, and signal analysis.