3D Reflector Localisation and Room Geometry Estimation using a Spherical Microphone Array

The analysis of room impulse responses to localise reflecting surfaces and estimate room ge- ometry is applicable in numerous aspects of acoustics, including source localisation, acoustic simulation, spatial audio, audio forensics, and room acoustic treatment. Geometry inference is an acoustic analysis problem where information about reflections extracted from impulse responses are used to localise reflective boundaries present in an environment, and thus estimate the geometry of the room. This problem however becomes more complex when considering non-convex rooms, as room shape can not be constrained to a subset of possible convex polygons. This paper presents a geometry inference method for localising reflective boundaries and inferring the room’s geometry for convex and non-convex room shapes. The method is tested using simulated room impulse responses for seven scenarios, and real-world room impulse responses measured in a cuboid-shaped room, using a spherical microphone array containing multiple spatially distributed channels capable of capturing both time- and direction-of-arrival. Results show that the general shape of the rooms is inferred for each case, with a higher degree of accuracy for convex shaped rooms. However, inaccuracies gen- erally arise as a result of the complexity of the room being inferred, or inaccurate estimation of time- and direction-of-arrival of reflections

Application of Machine Learning for the Spatial Analysis of Binaural Room Impulse Responses

Spatial impulse response analysis techniques are commonly used in the field of acoustics, as they help to characterise the interaction of sound with an enclosed environment. This paper presents a novel approach for spatial analyses of binaural impulse responses, using a binaural model fronted neural network. The proposed method uses binaural cues utilised by the human auditory system, which are mapped by the neural network to the azimuth direction of arrival classes. A cascade-correlation neural network was trained using a multi-conditional training dataset of head-related impulse responses with added noise. The neural network is tested using a set of binaural impulse responses captured using two dummy head microphones in an anechoic chamber, with a reflective boundary positioned to produce a reflection with a known direction of arrival. Results showed that the neural network was generalisable for the direct sound of the binaural room impulse responses for both dummy head microphones. However, it was found to be less accurate at predicting the direction of arrival of the reflections. The work indicates the potential of using such an algorithm for the spatial analysis of binaural impulse responses, while indicating where the method applied needs to be made more robust for more general application

The Impact of Gender on Conference Authorship in Audio Engineering : Analysis Using a New Data Collection Method

Contribution: This paper provides evidence of the lack of gender diversity at audio engineering conferences, using a novel and inclusive gender determination method to produce a new dataset of author gender. Background: Audio engineering has historically been male-dominated; whilst the number of non-male audio engineers has increased recently, the industry mindset has changed very little. Studies into the gender diversity of this field are required to force a shift in mindset and create a more inclusive environment. Research Questions: To what extent is there an imbalance in the representation of different genders at audio engineering conferences? Do conference topic, presentation type, or author position have an impact on the gender balance? Methodology: A novel method was designed to obtain pronouns of authors where possible, avoiding removal of data or potential false positives. The main limitation of this methodology is the time required for gender determination. Gender composition was analyzed across 20 conferences, with gender balance further analyzed within four key categories: conference topic, presentation type, position in the author byline, and the number of authors listed. Findings: This data-driven study demonstrates a clear lack of gender diversity in conference authorship in audio engineering. The results show low overall representation of non-male authors at audio engineering conferences, with significant differences across conference topics, and a notable lack of gender diversity within invited presentations. Index Terms— Audio Engineering, Conferences, Gender, Underrepresentation, Bias, Discrimination, STEM, Engineering Pipelin