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Found 9 papers, 4 papers with code
Leveraging Visual Supervision for Array-based Active Speaker Detection and Localization
The multichannel audio ``student'' network is trained to generate the same results.
Fusion of Audio and Visual Embeddings for Sound Event Localization and Detection
Sound event localization and detection (SELD) combines two subtasks: sound event detection (SED) and direction of arrival (DOA) estimation.
PAT: Position-Aware Transformer for Dense Multi-Label Action Detection
To address this issue, we (i) embed relative positional encoding in the self-attention mechanism and (ii) exploit multi-scale temporal relationships by designing a novel non hierarchical network, in contrast to the recent transformer-based approaches that use a hierarchical structure.
Ranked #1 on
Action Detection
on MultiTHUMOS
Audio Inputs for Active Speaker Detection and Localization via Microphone Array
This study considers the problem of detecting and locating an active talker's horizontal position from multichannel audio captured by a microphone array.
Tragic Talkers: A Shakespearean Sound- and Light-Field Dataset for Audio-Visual Machine Learning Research
This is partly due to the lack of available datasets enabling audio-visual research in this direction.
Visually Supervised Speaker Detection and Localization via Microphone Array
We propose to generate weak labels using a pre-trained active speaker detector on pre-extracted face tracks.
Single-Channel Signal Separation and Deconvolution with Generative Adversarial Networks
Single-channel signal separation and deconvolution aims to separate and deconvolve individual sources from a single-channel mixture and is a challenging problem in which no prior knowledge of the mixing filters is available.
Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging
For the unsupervised feature learning, we propose to use a symmetric or asymmetric deep de-noising auto-encoder (sDAE or aDAE) to generate new data-driven features from the Mel-Filter Banks (MFBs) features.
Fully DNN-based Multi-label regression for audio tagging
Compared with the conventional Gaussian Mixture Model (GMM) and support vector machine (SVM) methods, the proposed fully DNN-based method could well utilize the long-term temporal information with the whole chunk as the input.
