Deep Learning for Audio Signal Processing

Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.Comment: 15 pages, 2 pdf figure

Detecting Hidden Patterns in EEG Waveforms of Schizophrenia Patients using Convolutional Neural Network

Schizophrenia is a severe mental disorder that affects 1% of the world’s population and it is characterized by behavioral symptoms such as delusions, hallucinations and disorganized speech. The aim of this research was to develop an artificial intelligence model to detect hidden patterns in electroencephalogram (EEG) waveforms of schizophrenia patients. EEG waveforms of healthy subjects and schizophrenia patients were collected and processed. The data was used to develop a convolutional neural network (CNN) model which can automatically extract features and classify them. CNN does this by comparing the differences between the EEG waveforms of schizophrenia patients and healthy controls. These differences were used to train the classifier to differentiate the schizophrenia patients from the controls. The result of the CNN model showed a test accuracy of 60%, specificity of 55.55% and a precision of 55.55%. This early result shows that the model is promising. The next step will be to improve the accuracy of the model with a larger pool of data and many iterations, which is expected to lead to a better model that can be relied upon for schizophrenia diagnosis. In conclusion, CNN-based models like this one are relatively cheap and will improve the diagnosis of Schizophrenia, especially in low-income economies where the present study has been carried out