Binamix -- A Python Library for Generating Binaural Audio Datasets

2025-05-02

AI Summary

To address the low efficiency and poor reproducibility in generating binaural datasets for spatial audio research, this paper introduces Binamix—an open-source Python library built upon the SADIE II database (HRIRs/BRIRs from 20 subjects). Binamix supports multi-channel layouts, parametric spatial positioning, and large-scale synthesis. Its core innovation is an improved HRIR/BRIR interpolation method integrating enhanced Delaunay triangulation, enabling high-fidelity binaural rendering at arbitrary azimuth and elevation angles. The framework is modular, fully reproducible, and designed for downstream tasks—including codec evaluation and model training—featuring integrated example scripts, visualization tools, and utility functions. Released under the Apache 2.0 license, Binamix significantly advances standardization and development efficiency in spatial audio research.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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/

Problem

• Generates binaural audio datasets for spatial audio applications
• Provides HRIR/BRIR interpolation for missing angles using Delaunay triangulation
• Facilitates reproducible spatial audio research and machine learning training

Innovation

• Uses SADIE II Database for HRIR/BRIR data
• Implements Delaunay triangulation for HRIR interpolation
• Provides flexible multi-track mixing for spatial audio