Audio signal processing and its classification dates back to the past century. From speech recognition to speaker recognition and from speech to text conversion to music generation, a lot of advances has been made in this field using algorithms such as hidden Markov models, recurrent neural networks with long short-term memory layers (LSTM), deep convolutional neural networks (DCNNs), and the recent state-ofthe- art model for music and speech generation using WaveNets. These algorithms are applied after the audio signals are processed and effective feature extraction techniques are applied on them. Nowadays, devices come up with personal assistants with which they can interact either through text inputs or voice inputs. Most applications have also come up with voice search features, while some can generate transcripts from videos and recognize the song title when played. The constant urge for attaining perfection has also led to hybrid models combining supervised and unsupervised learning techniques for better feature extraction. The ability to deal with spectral and temporal data makes DCNNs different from deep neural networks and also makes it the appropriate choice to deal with speech data because correlation between words and phonemes are a characteristic of such data. The potentials of convolution neural networks are huge and being extended in areas like environmental sound classification, music and instrumental sound processing and classification, and large vocabulary continuous speech recognition. Therefore, this chapter gives detailed explanation about what an audio signal is and how it is processed. It will also cover the various feature extraction techniques and the classification algorithms. Finally, the presentday applications and the potentials of deep learning in this field will be explored. © 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved.