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Personal Sound Zone Rendering with Listener Individualization and Head Tracking
Personal Sound Zone Rendering with Listener Individualization and Head Tracking
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211153028
- ISBN
- 9798346759829
- DDC
- 534
- 저자명
- Qiao, Yue.
- 서명/저자
- Personal Sound Zone Rendering with Listener Individualization and Head Tracking
- 발행사항
- [Sl] : Princeton University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 178 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
- 주기사항
- Advisor: Choueiri, Edgar Yazid.
- 학위논문주기
- Thesis (Ph.D.)--Princeton University, 2024.
- 초록/해제
- 요약The impact of listener variability factors on the performance of personal sound zone (PSZ) rendering is investigated, and methods for incorporating listener individualization and head tracking into PSZ systems are proposed. PSZ rendering allows different audio programs to be delivered to listeners in the same space with minimal interference, using loudspeakers equipped with digital filters. A significant challenge in PSZ rendering is the mismatch between the acoustic transfer functions (ATFs) used in filter design and those in the actual environment, particularly due to listener variability factors, which have not been previously studied.Two categories of listener variability factors are considered: variations in listeners' anthropometric features (e.g., head and torso shapes) and listener head movements. For static PSZs, the impact of individualizing binaural room transfer functions (BRTFs) on PSZ performance is experimentally investigated using metrics such as Inter-Zone Isolation and Inter-Program Isolation. Results show that individualizing BRTFs can significantly improve isolation, but at the cost of reduced robustness against head misalignments. Additionally, an inter-listener BRTF coupling effect is observed, which can negatively impact performance for both listeners when a single listener's BRTFs are mismatched.For head-tracked PSZ rendering, requirements for the spatial sampling of BRTFs concerning listener head translations and rotations are explored. The required sampling resolution is found to depend on factors such as the moving listener's position, the frequency band of the rendered audio, and perturbations caused by the other listener. Furthermore, a deep learning-based framework using a spatially adaptive neural network (SANN) is proposed and evaluated. The SANN model takes head positions as input and outputs the corresponding PSZ filter coefficients. It can be trained either with simulated ATFs with data augmentation for robustness in uncertain environments or with a mix of simulated and measured ATFs for customization under known conditions. Compared to traditional filter design methods, SANN is shown to provide similar or better isolation performance in unknown rendering environments while being more efficient in computation and storage, making it suitable for real-time rendering of PSZs that adapt to listeners' concurrent head movements.
- 일반주제명
- Acoustics
- 일반주제명
- Electrical engineering
- 일반주제명
- Aerospace engineering
- 키워드
- Head tracking
- 키워드
- Spatial audio
- 기타저자
- Princeton University Mechanical and Aerospace Engineering
- 기본자료저록
- Dissertations Abstracts International. 86-06B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211153028
■006m o d
■007cr#unu||||||||
■020 ▼a9798346759829
■035 ▼a(MiAaPQ)AAI31634517
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a534
■1001 ▼aQiao, Yue.▼0(orcid)0000-0003-1095-3115
■24510▼aPersonal Sound Zone Rendering with Listener Individualization and Head Tracking
■260 ▼a[Sl]▼bPrinceton University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a178 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-06, Section: B.
■500 ▼aAdvisor: Choueiri, Edgar Yazid.
■5021 ▼aThesis (Ph.D.)--Princeton University, 2024.
■520 ▼aThe impact of listener variability factors on the performance of personal sound zone (PSZ) rendering is investigated, and methods for incorporating listener individualization and head tracking into PSZ systems are proposed. PSZ rendering allows different audio programs to be delivered to listeners in the same space with minimal interference, using loudspeakers equipped with digital filters. A significant challenge in PSZ rendering is the mismatch between the acoustic transfer functions (ATFs) used in filter design and those in the actual environment, particularly due to listener variability factors, which have not been previously studied.Two categories of listener variability factors are considered: variations in listeners' anthropometric features (e.g., head and torso shapes) and listener head movements. For static PSZs, the impact of individualizing binaural room transfer functions (BRTFs) on PSZ performance is experimentally investigated using metrics such as Inter-Zone Isolation and Inter-Program Isolation. Results show that individualizing BRTFs can significantly improve isolation, but at the cost of reduced robustness against head misalignments. Additionally, an inter-listener BRTF coupling effect is observed, which can negatively impact performance for both listeners when a single listener's BRTFs are mismatched.For head-tracked PSZ rendering, requirements for the spatial sampling of BRTFs concerning listener head translations and rotations are explored. The required sampling resolution is found to depend on factors such as the moving listener's position, the frequency band of the rendered audio, and perturbations caused by the other listener. Furthermore, a deep learning-based framework using a spatially adaptive neural network (SANN) is proposed and evaluated. The SANN model takes head positions as input and outputs the corresponding PSZ filter coefficients. It can be trained either with simulated ATFs with data augmentation for robustness in uncertain environments or with a mix of simulated and measured ATFs for customization under known conditions. Compared to traditional filter design methods, SANN is shown to provide similar or better isolation performance in unknown rendering environments while being more efficient in computation and storage, making it suitable for real-time rendering of PSZs that adapt to listeners' concurrent head movements.
■590 ▼aSchool code: 0181.
■650 4▼aAcoustics
■650 4▼aElectrical engineering
■650 4▼aAerospace engineering
■653 ▼aArray signal processing
■653 ▼aHead tracking
■653 ▼aListener individualization
■653 ▼aPersonal sound zones
■653 ▼aSound field control
■653 ▼aSpatial audio
■690 ▼a0986
■690 ▼a0544
■690 ▼a0538
■71020▼aPrinceton University▼bMechanical and Aerospace Engineering.
■7730 ▼tDissertations Abstracts International▼g86-06B.
■790 ▼a0181
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164659▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
