In this project, I utilized Pyroom Acoustics, a Python package, to address the challenge of acquiring acoustic data for machine learning applications in scenarios where practical or financial constraints hinder direct data gathering. The aim was to provide a solution that ensures sufficient data availability for implementing machine learning algorithms in diverse acoustic environments. By leveraging Pyroom Acoustics, I generated synthetic room impulse responses (RIRs) that accurately simulate various room sizes, acoustic properties, and microphone/speaker configurations

The methodology involved defining room geometries, specifying material properties, and positioning microphones and speakers within virtual room environments using Pyroom Acoustics. By systematically varying these parameters, I generated a comprehensive dataset comprising RIRs for a wide range of acoustic scenarios. This approach not only eliminated the need for expensive data collection setups but also provided a scalable solution for generating diverse acoustic data tailored to specific research or industrial requirements.