Low energy deep-learning networks for generating auditory features for audio processing pipelines

Abstract

Low energy deep-learning networks for generating auditory features such as mel frequency cepstral coefficients in audio processing pipelines are provided. In various embodiments, a first neural network is trained to output auditory features such as mel-frequency cepstral coefficients, linear predictive coding coefficients, perceptual linear predictive coefficients, spectral coefficients, filter bank coefficients, and/or spectro-temporal receptive fields based on input audio samples. A second neural network is trained to output a classification based on input auditory features such as mel-frequency cepstral coefficients. An input audio sample is provided to the first neural network. Auditory features such as mel-frequency cepstral coefficients are received from the first neural network. The auditory features such as mel-frequency cepstral coefficients are provided to the second neural network. A classification of the input audio sample is received from the second neural network.