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Convolutional neural networks for audio processing: starting pack
| Title | Convolutional neural networks for audio processing: starting pack |
| Publication Type | Miscellaneous |
| Year of Publication | 2017 |
| Authors | Miron, M. , & Slizovskaia O. |
| preprint/postprint document | https://pydata.org/barcelona2017/schedule/presentation/37/ |
| Full Text |
Neural networks are increasingly popular in audio signal processing for topics as speech recognition or denoising. Scientific papers are usually accompanied by code repositories which rely on libraries as Theano or Tensorflow that can be interfaced from python. However, adapting a system to different tasks and data must take into account a set of pre-training routines and parameter debugging which we will discuss in this tutorial. Starting from the audio signals we introduce the data pre-training steps (feature computation, batch generation, normalization( with examples in numpy or scipy. We summarize the core concepts in neural networks and we code an architecture with the Keras library. Finally, we learn how to visualize and debug parameters with TensorBoard. The slides can be found here . The notebooks can be found at the github repository . |
