A system comprising one or more processors (210, 212, 260) and a machine learning classifier model that comprises a neural network, wherein the machine learning classifier model is formed by a machine-based method comprising receiving a first number of sound records, each of the sound records representing respiratory sounds of a subject acquired by auscultation, each of the sound records having known sound classes determined by one or more experts, wherein the sound classes comprise normal sound, wheezes, rhonchi, fine crackles, coarse crackles, skin rubbing artifacts, interference artifacts, and heartbeat artifacts, pre-training initial convolutional layers of a neural network using a second number of known spectrograms, after the pre-training, training the initial convolutional layers of the neural network using the first number of sound records and the known sound classes, wherein the second number of sound records is at least an order of magnitude larger than the first number of sound records, the one or more processors configured to: receive a sound record representing respiratory sounds of a subject acquired by auscultation, transform the received sound record into a time-frequency domain graphical representation that comprises a Mel spectrogram, determine a sound class for the respiratory sounds of the subject by applying the time-frequency domain graphical representation to the machine learning classifier model, infer a respiratory condition of the subject based at least on the sound class determined by the machine learning classifier model; and present information about the inferred respiratory condition through a user interface of a device. receiving a first number of sound records, each of the sound records representing respiratory sounds of a subject acquired by auscultation, each of the sound records having known sound classes determined by one or more experts, wherein the sound classes comprise normal sound, wheezes, rhonchi, fine crackles, coarse crackles, skin rubbing artifacts, interference artifacts, and heartbeat artifacts, pre-training initial convolutional layers of a neural network using a second number of known spectrograms, after the pre-training, training the initial convolutional layers of the neural network using the first number of sound records and the known sound classes, wherein the second number of sound records is at least an order of magnitude larger than the first number of sound records, the one or more processors configured to: receive a sound record representing respiratory sounds of a subject acquired by auscultation, transform the received sound record into a time-frequency domain graphical representation that comprises a Mel spectrogram, determine a sound class for the respiratory sounds of the subject by applying the time-frequency domain graphical representation to the machine learning classifier model, infer a respiratory condition of the subject based at least on the sound class determined by the machine learning classifier model; and present information about the inferred respiratory condition through a user interface of a device. receive a sound record representing respiratory sounds of a subject acquired by auscultation, transform the received sound record into a time-frequency domain graphical representation that comprises a Mel spectrogram, determine a sound class for the respiratory sounds of the subject by applying the time-frequency domain graphical representation to the machine learning classifier model, infer a respiratory condition of the subject based at least on the sound class determined by the machine learning classifier model; and present information about the inferred respiratory condition through a user interface of a device.