Blind Acoustic Room Parameter Estimation Using Phase Features

Ick, Christopher; Mehrabi, Adib; Jin, Wenyu

doi:10.1109/icassp49357.2023.10094848

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Other1

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Phase Continuity-Aware Self-Attentive Recurrent Network with Adaptive Feature Selection for Robust VAD

Tang,

Huang,

Zhang

et al. 2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

Phase Continuity-Aware Self-Attentive Recurrent Network with Adaptive Feature Selection for Robust VAD

Tang,

Huang,

Zhang

et al. 2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

Self-Supervised Learning of Spatial Acoustic Representation With Cross-Channel Signal Reconstruction and Multi-Channel Conformer

Yang,

2024

IEEE/ACM Trans. Audio Speech Lang. Process.

View full text Add to dashboard Cite

An end-to-end approach for blindly rendering a virtual sound source in an audio augmented reality environment

Saini,

Engel,

Peissig

2024

J AUDIO SPEECH MUSIC PROC.

View full text Add to dashboard Cite

Audio augmented reality (AAR), a prominent topic in the field of audio, requires understanding the listening environment of the user for rendering an authentic virtual auditory object. Reverberation time ($$RT_{60}$$ R T 60 ) is a predominant metric for the characterization of room acoustics and numerous approaches have been proposed to estimate it blindly from a reverberant speech signal. However, a single $$RT_{60}$$ R T 60 value may not be sufficient to correctly describe and render the acoustics of a room. This contribution presents a method for the estimation of multiple room acoustic parameters required to render close-to-accurate room acoustics in an unknown environment. It is shown how these parameters can be estimated blindly using an audio transformer that can be deployed on a mobile device. Furthermore, the paper also discusses the use of the estimated room acoustic parameters to find a similar room from a dataset of real BRIRs that can be further used for rendering the virtual audio source. Additionally, a novel binaural room impulse response (BRIR) augmentation technique to overcome the limitation of inadequate data is proposed. Finally, the proposed method is validated perceptually by means of a listening test.

show abstract

Blind Acoustic Room Parameter Estimation Using Phase Features

Cited by 4 publications

References 19 publications

Phase Continuity-Aware Self-Attentive Recurrent Network with Adaptive Feature Selection for Robust VAD

Phase Continuity-Aware Self-Attentive Recurrent Network with Adaptive Feature Selection for Robust VAD

Self-Supervised Learning of Spatial Acoustic Representation With Cross-Channel Signal Reconstruction and Multi-Channel Conformer

An end-to-end approach for blindly rendering a virtual sound source in an audio augmented reality environment

Contact Info

Product

Resources

About