Effect of acoustic scene complexity and visual scene representation on auditory perception in virtual audio-visual environments
In daily life, social interaction and acoustic communication often take place in complex acoustic environments (CAE) with a variety of interfering sounds and reverberation. For hearing research and the evaluation of hearing systems, simulated CAEs using virtual reality techniques have gained interest in the context of ecological validity. In the current study, the effect of scene complexity and visual representation of the scene on psychoacoustic measures like sound source location, distance perception, loudness, speech intelligibility, and listening effort in a virtual audio-visual environment was investigated. A 3-dimensional, 86-channel loudspeaker array was used to render the sound field in combination with or without a head-mounted display (HMD) to create an immersive stereoscopic visual representation of the scene. The scene consisted of a ring of eight (virtual) loudspeakers which played a target speech stimulus and nonsense speech interferers in several spatial conditions. Either an anechoic (snowy outdoor scenery) or echoic environment (loft apartment) with a reverberation time (T60) of about 1.5 s was simulated. In addition to varying the number of interferers, scene complexity was varied by assessing the psychoacoustic measures in isolated consecutive measurements orcsimultaneously. Results showed no significant effect of wearing the HMD on the data. Loudness and distance perception showed significantly different results when they were measured simultaneously instead of consecutively in isolation. The advantage of the suggested setup is that it can be directly transferred to a corresponding real room, enabling a 1:1 comparison and verification of the perception experiments in the real and virtual environment.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Robust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Context
In this paper, we present a comprehensive analysis of Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording from its acoustic signature. In particular, we firstly propose an incepti…
Acoustic Scene ClassificationScene ClassificationDD-CNN: Depthwise Disout Convolutional Neural Network for Low-complexity Acoustic Scene Classification
This paper presents a Depthwise Disout Convolutional Neural Network (DD-CNN) for the detection and classification of urban acoustic scenes. Specifically, we use log-mel as feature representations of acoustic signals for …
Acoustic Scene ClassificationClassificationGeneral ClassificationScene ClassificationA Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification
We propose a novel neural model compression strategy combining data augmentation, knowledge transfer, pruning, and quantization for device-robust acoustic scene classification (ASC). Specifically, we tackle the ASC task …
Acoustic Scene ClassificationData AugmentationModel CompressionNetwork Pruning+3From Visual to Acoustic Question Answering
We introduce the new task of Acoustic Question Answering (AQA) to promote research in acoustic reasoning. The AQA task consists of analyzing an acoustic scene composed by a combination of elementary sounds and answering …
Acoustic Question AnsweringPositionQuestion AnsweringVisual ReasoningLow-complexity CNNs for Acoustic Scene Classification
This technical report describes the SurreyAudioTeam22s submission for DCASE 2022 ASC Task 1, Low-Complexity Acoustic Scene Classification (ASC). The task has two rules, (a) the ASC framework should have maximum 128K para…
Acoustic Scene ClassificationClassificationScene Classification