arXiv:2104.13276
MULTIMODAL ANALYSIS: Informed content estimation and audio source separation
원문 초록
This dissertation proposes the study of multimodal learning in the context of musical signals. Throughout, we focus on the interaction between audio signals and text information. Among the many text sources related to music that can be used (e.g. reviews, metadata, or social network feedback), we concentrate on lyrics. The singing voice directly connects the audio signal and the text information in a unique way, combining melody and lyrics where a linguistic dimension complements the abstraction of musical instruments. Our study focuses on the audio and lyrics interaction for targeting source separation and informed content estimation.
함께 살펴볼 논문
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Towards Reliable Objective Evaluation Metrics for Generative Singing Voice Separation Models
Training-Free Multi-Step Audio Source Separation
Leveraging LLM and Text-Queried Separation for Noise-Robust Sound Event Detection
Exploring Text-Queried Sound Event Detection with Audio Source Separation
Unsupervised Composable Representations for Audio
Facing the Music: Tackling Singing Voice Separation in Cinematic Audio Source Separation
토론
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