• Corpus ID: 235623893

SofaMyRoom: a fast and multiplatform "shoebox" room simulator for binaural room impulse response dataset generation

  title={SofaMyRoom: a fast and multiplatform "shoebox" room simulator for binaural room impulse response dataset generation},
  author={Roberto Barumerli and Daniele Bianchi and Michele Geronazzo and Federico Avanzini},
This paper introduces a shoebox room simulator able to systematically generate synthetic datasets of binaural room impulse responses (BRIRs) given an arbitrary set of head-related transfer functions (HRTFs). The evaluation of machine hearing algorithms frequently requires BRIR datasets in order to simulate the acoustics of any environment. However, currently available solutions typically consider only HRTFs measured on dummy heads, which poorly characterize the high variability in spatial sound… 

Figures and Tables from this paper



Hearing in a shoe-box: Binaural source position and wall absorption estimation using virtually supervised learning

A new framework for supervised sound source localization referred to as virtually-supervised learning is introduced and results indicate that this mapping successfully estimates the azimuth and elevation of new sources, but also their range and even the walls' absorption coefficients solely based on binaural signals.

A fast and accurate “shoebox” room acoustics simulator

A new “shoebox” room acoustics simulator is presented that is designed to support research into signal processing algorithms that are robust to reverberation and is computationally fast, portable to many kinds of research environments, and flexible to use.

Auditory models comparison for horizontal localization of concurrent speakers in adverse acoustic scenarios

This paper comparing and reproducing the predictions of two public available computational auditory models for speaker localization in different simulated environments shows a good agreement with previous literature and the machine learning approach emphasizes peculiarities of each approach for auditory peripheral processing.

A Standardized Repository of Head-Related and Headphone Impulse Response Data

The structure of the repository is an improvement with respect to the MARL-NYU data format, born as an attempt to unify HRIR databases and supports flexible analysis and synthesis processes and robust headphone equalization.

A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation

This paper proposes to analyze a large number of established and recent techniques according to four transverse axes: 1) the acoustic impulse response model, 2) the spatial filter design criterion, 3) the parameter estimation algorithm, and 4) optional postfiltering.

VAST: The Virtual Acoustic Space Traveler Dataset

It is shown that virtually-learned mappings on this dataset generalize to real data, overcoming some intrinsic limitations of traditional binaural sound localization methods based on time differences of arrival.

Localization of virtual sound sources with bilateral hearing aids in realistic acoustical scenes.

The results showed that the system for generating virtual environments is a reliable tool to evaluate sound localization with hearing aids and the beamformer allowed the listener to resolve the front-back ambiguity.

Acoustic Space Learning for Sound-Source Separation and Localization on Binaural Manifolds

This paper proposes a probabilistic piecewise affine mapping model (PPAM) specifically designed to deal with high-dimensional data exhibiting an intrinsic piecewise linear structure and derives a closed-form expectation-maximization (EM) procedure for estimating the model parameters, followed by Bayes inversion for obtaining the full posterior density function of a sound-source direction.

Image method for efficiently simulating small‐room acoustics

The theoretical and practical use of image techniques for simulating the impulse response between two points in a small rectangular room, when convolved with any desired input signal, simulates room reverberation of the input signal.