Digital system for sound sources location problem in multidimensional space using machine learning methods

Abstract

The issues presented in this work relate to the possibility of detecting the location of the sound source and its effectiveness in production conditions using recordings made with artificial head and processed with using an artificial neural networks. The use of an artificial head and artificial neural networks was motivated by an attempt to map human perception using available computer technology. The work attempts to map the features used for location human sources of sound through digital signal processing and machine learning. Artificial neural networks are commonly used, among others, in image recognition, where, using a camera or camera, the algorithm classifies objects in the image. Machine learning algorithms in addition allow for the implementation of self-learning speech or text recognition models. As part of the work, it was decided to localize the sound for a collection of fifteen different items sources relative to the head. To avoid duplicating similar measurements, the sound source was to the left of the artificial head. For signal processing and programming language was used to prepare the artificial neural network model Python with the Numpy numerical library, a library designed for signal processing Scipy, TensorFlow and Keras.