Binamix -- A Python Library for Generating Binaural Audio Datasets

TL;DR

Binamix is an open-source Python library that simplifies the creation of large-scale binaural audio datasets using the SADIE II database, supporting various spatial configurations for research and development in spatial audio applications.

Contribution

The paper introduces Binamix, a novel Python library that provides flexible, reproducible tools for binaural dataset generation leveraging HRIR and BRIR data, with interpolation and multi-track mixing capabilities.

Findings

Enables programmatic binaural dataset creation with high flexibility.

Supports accurate HRIR/BRIR interpolation using modified Delaunay triangulation.

Facilitates large-scale spatial audio data generation for research and testing.

Abstract

The increasing demand for spatial audio in applications such as virtual reality, immersive media, and spatial audio research necessitates robust solutions to generate binaural audio data sets for use in testing and validation. Binamix is an open-source Python library designed to facilitate programmatic binaural mixing using the extensive SADIE II Database, which provides Head Related Impulse Response (HRIR) and Binaural Room Impulse Response (BRIR) data for 20 subjects. The Binamix library provides a flexible and repeatable framework for creating large-scale spatial audio datasets, making it an invaluable resource for codec evaluation, audio quality metric development, and machine learning model training. A range of pre-built example scripts, utility functions, and visualization plots further streamline the process of custom pipeline creation. This paper presents an overview of the library's capabilities, including binaural rendering, impulse response interpolation, and multi-track mixing for various speaker layouts. The tools utilize a modified Delaunay triangulation technique to achieve accurate HRIR/BRIR interpolation where desired angles are not present in the data. By supporting a wide range of parameters such as azimuth, elevation, subject Impulse Responses (IRs), speaker layouts, mixing controls, and more, the library enables researchers to create large binaural datasets for any downstream purpose. Binamix empowers researchers and developers to advance spatial audio applications with reproducible methodologies by offering an open-source solution for binaural rendering and dataset generation. We release the library under the Apache 2.0 License at https://github.com/QxLabIreland/Binamix/