Text-Queried Target Sound Event Localization
원문 초록
Sound event localization and detection (SELD) aims to determine the appearance of sound classes, together with their Direction of Arrival (DOA). However, current SELD systems can only predict the activities of specific classes, for example, 13 classes in DCASE challenges. In this paper, we propose text-queried target sound event localization (SEL), a new paradigm that allows the user to input the text to describe the sound event, and the SEL model can predict the location of the related sound event. The proposed task presents a more user-friendly way for human-computer interaction. We provide a benchmark study for the proposed task and perform experiments on datasets created by simulated room impulse response (RIR) and real RIR to validate the effectiveness of the proposed methods. We hope that our benchmark will inspire the interest and additional research for text-queried sound source localization.
함께 살펴볼 논문
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Sound Event Localization and Detection for Real Spatial Sound Scenes: Event-Independent Network and Data Augmentation Chains
Feature Aggregation in Joint Sound Classification and Localization Neural Networks
ReverbFX: A Dataset of Room Impulse Responses Derived from Reverb Effect Plugins for Singing Voice Dereverberation
Hearing and Seeing Through CLIP: A Framework for Self-Supervised Sound Source Localization
ArrayDPS: Unsupervised Blind Speech Separation with a Diffusion Prior
Reverberation-based Features for Sound Event Localization and Detection with Distance Estimation
토론
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