Learning Multiple Sound Source 2D Localization
원문 초록
In this paper, we propose novel deep learning based algorithms for multiple sound source localization. Specifically, we aim to find the 2D Cartesian coordinates of multiple sound sources in an enclosed environment by using multiple microphone arrays. To this end, we use an encoding-decoding architecture and propose two improvements on it to accomplish the task. In addition, we also propose two novel localization representations which increase the accuracy. Lastly, new metrics are developed relying on resolution-based multiple source association which enables us to evaluate and compare different localization approaches. We tested our method on both synthetic and real world data. The results show that our method improves upon the previous baseline approach for this problem.
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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
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