Abstract: Provided is an apparatus for automatically classifying an input sound source according to a preset criterion and are an apparatus and method for automatically classifying a sound source according to a set criterion by using deep learning. The apparatus for classifying a sound source includes a processor and a memory connected to the processor and storing a deep learning algorithm and original sound data, wherein the memory stores program instructions executable by the processor to generate n pieces of image data corresponding to the original sound data according to a preset method, generate training image data corresponding to the original sound data by using the n pieces of image data, train the deep learning algorithm by using the training image data, and classify target sound data according to a preset criterion by using the deep learning algorithm, wherein the n is a natural number greater than or equal to 2.

Abstract: Provided herein are methods, apparatuses, and non-transitory computer-readable recording media for measuring photoplethysmography (PPG) signals. Accurate PPG signals can be obtained even in a situation in which brightness of ambient light is not constant due to external light sources, and accuracy of a variety of biological information derivable from the PPG signals can be improved.