SoundFactory

Abstract

The proliferation of smart connected devices using digital assistants activated by voice commands (e.g., Apple Siri, Google Assistant, Amazon Alexa, etc.) is raising the interest in algorithms to localize and recognize audio sources. Among the others, deep neural networks (DNNs) are seen as a promising approach to accomplish such task. Unlike other approaches, DNNs can categorize received events, thus discriminating between events of interests and not even in presence of noise. Despite their advantages, DNNs require large datasets to be trained. Thus, tools for generating datasets are of great value, being able to accelerate the development of advanced learning models. This paper presents SoundFactory, a framework for simulating the propagation of sound waves (also considering noise, reverberation, reflection, attenuation, and other interfering waves) and the microphone array response to such sound waves. As such, SoundFactory allows to easily generate datasets to train deep neural networks which are at the basis of modern applications. SoundFactory is flexible enough to simulate many different microphone array configurations, thus covering a large set of use cases. To demonstrate the capabilities offered by SoundFactory, we generated a dataset and trained two different (rather simple) learning models against them, achieving up to 97% of accuracy. The quality of the generated dataset has been also assessed comparing the microphone array model responses with the real ones.