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Machine Learning in Acoustic Signal Processing

Machine Learning in Acoustic Signal Processing

This video was recorded at Machine Learning Summer School (MLSS), Chicago 2009. This tutorial presents a framework for understanding and comparing applications of pattern recognition in acoustic signal processing. Representative applications will be delimited by two binary features: (1) regression vs. (2) classification (inferred variables are continuous vs. discrete), (A) instantaneous vs. (B) dynamic. (1. Regression) problems include imaging and sound source tracking using a device with unknown properties, and inverse problems, e.g., articulatory estimation from speech audio. (2. Classification) problems include, e.g., the detection of syllable onsets and offsets in a speech signal, and the classification of non-speech audio events. (A. Instantaneous) inference is performed using a... Show More


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