The trajectoRIR Database: Room Acoustic Recordings Along a Trajectory of Moving Microphones
원문 초록
Data availability is essential to develop acoustic signal processing algorithms, especially when it comes to data-driven approaches that demand large and diverse training datasets. For this reason, an increasing number of databases have been published in recent years, including either room impulse responses (RIRs) or audio recordings during motion. In this paper we introduce the trajectoRIR database, an extensive, multi-array collection of both dynamic and stationary acoustic recordings along a controlled trajectory in a room. Specifically, the database features recordings using moving microphones and stationary RIRs spatially sampling the room acoustics along an L-shaped trajectory. This combination makes trajectoRIR unique and applicable in various tasks ranging from sound source localization and tracking to spatially dynamic sound field reconstruction, auralization and system identification. The recording room has a reverberation time of 0.5 seconds, and the three different microphone configurations employed include a dummy head, with additional reference microphones located next to the ears, 3 first-order Ambisonics microphones, two circular arrays of 16 and 4 channels, and a 12-channel linear array. The motion of the microphones was achieved using a robotic cart traversing a 4.62 meter-long rail at three speeds: [0.2, 0.4, 0.8] m/s. Audio signals were reproduced using two stationary loudspeakers. The collected database features 8648 stationary RIRs, as well as perfect sweeps, speech, music, and stationary noise recorded during motion. Python functions are included to access the recorded audio as well as to retrieve geometrical information.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Hearing and Seeing Through CLIP: A Framework for Self-Supervised Sound Source Localization
Sound2Vision: Generating Diverse Visuals from Audio through Cross-Modal Latent Alignment
A Critical Assessment of Visual Sound Source Localization Models Including Negative Audio
Enhancing Sound Source Localization via False Negative Elimination
wav2pos: Sound Source Localization using Masked Autoencoders
Aligning Sight and Sound: Advanced Sound Source Localization Through Audio-Visual Alignment
토론
이 논문과 연결된 포럼 글이 아직 없습니다.