Deep learning for audio and music

This chapter provides an overview of how deep learning techniques can be used for audio signals. We first review the main DNN architectures, meta-architectures and training paradigms used for audio processing. By highlighting the specifies of the audio signal, we discuss the various possible audio representations to be used as input of a DNN-time and frequency representations, waveform representations and knowledge-driven representations-and discuss how the first layers of a DNN can be set to take into account these specificity's. We then review a set of applications for three main classes of problems: audio recognition, audio processing and audio generation. We do this considering two types of audio content which are less commonly addressed in the literature: music and environmental sounds.