Separating Invisible Sounds Toward Universal Audiovisual Scene-Aware Sound Separation

The audio-visual sound separation field assumes visible sources in videos, but this excludes invisible sounds beyond the camera's view. Current methods struggle with such sounds lacking visible cues. This paper introduces a novel "Audio-Visual Scene-Aware Separation" (AVSA-Sep) framework. It includes a semantic parser for visible and invisible sounds and a separator for scene-informed separation. AVSA-Sep successfully separates both sound types, with joint training and cross-modal alignment enhancing effectiveness.Comment: Accepted at ICCV 2023 - AV4D, 4 figures, 3 table

Visually Guided Sound Source Separation using Cascaded Opponent Filter Network

The objective of this paper is to recover the original component signals from a mixture audio with the aid of visual cues of the sound sources. Such task is usually referred as visually guided sound source separation. The proposed Cascaded Opponent Filter (COF) framework consists of multiple stages, which recursively refine the source separation. A key element in COF is a novel opponent filter module that identifies and relocates residual components between sources. The system is guided by the appearance and motion of the source, and, for this purpose, we study different representations based on video frames, optical flows, dynamic images, and their combinations. Finally, we propose a Sound Source Location Masking (SSLM) technique, which, together with COF, produces a pixel level mask of the source location. The entire system is trained end-to-end using a large set of unlabelled videos. We compare COF with recent baselines and obtain the state-of-the-art performance in three challenging datasets (MUSIC, A-MUSIC, and A-NATURAL). Project page: https://ly-zhu.github.io/cof-net.Comment: main paper 14 pages, ref 3 pages, and supp 7 pages. Revised argument in section 3 and

V-SlowFast Network for Efficient Visual Sound Separation

The objective of this paper is to perform visual sound separation: i) we study visual sound separation on spectrograms of different temporal resolutions; ii) we propose a new light yet efficient three-stream framework V-SlowFast that operates on Visual frame, Slow spectrogram, and Fast spectrogram. The Slow spectrogram captures the coarse temporal resolution while the Fast spectrogram contains the fine-grained temporal resolution; iii) we introduce two contrastive objectives to encourage the network to learn discriminative visual features for separating sounds; iv) we propose an audio-visual global attention module for audio and visual feature fusion; v) the introduced V-SlowFast model outperforms previous state-of-the-art in single-frame based visual sound separation on small- and large-scale datasets: MUSIC-21, AVE, and VGG-Sound. We also propose a small V-SlowFast architecture variant, which achieves 74.2% reduction in the number of model parameters and 81.4% reduction in GMACs compared to the previous multi-stage models. Project page: https://ly-zhu.github.io/V-SlowFastacceptedVersionPeer reviewe

GRASS: Generative Recursive Autoencoders for Shape Structures

We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures. Our key insight is that 3D shapes are effectively characterized by their hierarchical organization of parts, which reflects fundamental intra-shape relationships such as adjacency and symmetry. We develop a recursive neural net (RvNN) based autoencoder to map a flat, unlabeled, arbitrary part layout to a compact code. The code effectively captures hierarchical structures of man-made 3D objects of varying structural complexities despite being fixed-dimensional: an associated decoder maps a code back to a full hierarchy. The learned bidirectional mapping is further tuned using an adversarial setup to yield a generative model of plausible structures, from which novel structures can be sampled. Finally, our structure synthesis framework is augmented by a second trained module that produces fine-grained part geometry, conditioned on global and local structural context, leading to a full generative pipeline for 3D shapes. We demonstrate that without supervision, our network learns meaningful structural hierarchies adhering to perceptual grouping principles, produces compact codes which enable applications such as shape classification and partial matching, and supports shape synthesis and interpolation with significant variations in topology and geometry.Comment: Corresponding author: Kai Xu ([email protected]