Neural Network Based Reinforcement Learning for Audio-Visual Gaze Control in Human-Robot Interaction

This paper introduces a novel neural network-based reinforcement learning approach for robot gaze control. Our approach enables a robot to learn and to adapt its gaze control strategy for human-robot interaction neither with the use of external sensors nor with human supervision. The robot learns to focus its attention onto groups of people from its own audio-visual experiences, independently of the number of people, of their positions and of their physical appearances. In particular, we use a recurrent neural network architecture in combination with Q-learning to find an optimal action-selection policy; we pre-train the network using a simulated environment that mimics realistic scenarios that involve speaking/silent participants, thus avoiding the need of tedious sessions of a robot interacting with people. Our experimental evaluation suggests that the proposed method is robust against parameter estimation, i.e. the parameter values yielded by the method do not have a decisive impact on the performance. The best results are obtained when both audio and visual information is jointly used. Experiments with the Nao robot indicate that our framework is a step forward towards the autonomous learning of socially acceptable gaze behavior.Comment: Paper submitted to Pattern Recognition Letter

Audio-visual tracking of concurrent speakers

Audio-visual tracking of an unknown number of concurrent speakers in 3D is a challenging task, especially when sound and video are collected with a compact sensing platform. In this paper, we propose a tracker that builds on generative and discriminative audio-visual likelihood models formulated in a particle filtering framework. We localize multiple concurrent speakers with a de-emphasized acoustic map assisted by the image detection-derived 3D video observations. The 3D multimodal observations are either assigned to existing tracks for discriminative likelihood computation or used to initialize new tracks. The generative likelihoods rely on color distribution of the target and the de-emphasized acoustic map value. Experiments on AV16.3 and CAV3D datasets show that the proposed tracker outperforms the uni-modal trackers and the state-of-the-art approaches both in 3D and on the image plane

SALSA: A Novel Dataset for Multimodal Group Behavior Analysis

Studying free-standing conversational groups (FCGs) in unstructured social settings (e.g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels. However, analyzing social scenes involving FCGs is also highly challenging due to the difficulty in extracting behavioral cues such as target locations, their speaking activity and head/body pose due to crowdedness and presence of extreme occlusions. To this end, we propose SALSA, a novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis, and make two main contributions to research on automated social interaction analysis: (1) SALSA records social interactions among 18 participants in a natural, indoor environment for over 60 minutes, under the poster presentation and cocktail party contexts presenting difficulties in the form of low-resolution images, lighting variations, numerous occlusions, reverberations and interfering sound sources; (2) To alleviate these problems we facilitate multimodal analysis by recording the social interplay using four static surveillance cameras and sociometric badges worn by each participant, comprising the microphone, accelerometer, bluetooth and infrared sensors. In addition to raw data, we also provide annotations concerning individuals' personality as well as their position, head, body orientation and F-formation information over the entire event duration. Through extensive experiments with state-of-the-art approaches, we show (a) the limitations of current methods and (b) how the recorded multiple cues synergetically aid automatic analysis of social interactions. SALSA is available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure

A study of lip movements during spontaneous dialog and its application to voice activity detection

International audienceThis paper presents a quantitative and comprehensive study of the lip movements of a given speaker in different speech/nonspeech contexts, with a particular focus on silences i.e., when no sound is produced by the speaker . The aim is to characterize the relationship between "lip activity" and "speech activity" and then to use visual speech information as a voice activity detector VAD . To this aim, an original audiovisual corpus was recorded with two speakers involved in a face-to-face spontaneous dialog, although being in separate rooms. Each speaker communicated with the other using a microphone, a camera, a screen, and headphones. This system was used to capture separate audio stimuli for each speaker and to synchronously monitor the speaker's lip movements. A comprehensive analysis was carried out on the lip shapes and lip movements in either silence or nonsilence i.e., speech+nonspeech audible events . A single visual parameter, defined to characterize the lip movements, was shown to be efficient for the detection of silence sections. This results in a visual VAD that can be used in any kind of environment noise, including intricate and highly nonstationary noises, e.g., multiple and/or moving noise sources or competing speech signals

Audio-Visual Learning for Scene Understanding

Multimodal deep learning aims at combining the complementary information of different modalities. Among all modalities, audio and video are the predominant ones that humans use to explore the world. In this thesis, we decided to focus our study on audio-visual deep learning to mimic with our networks how humans perceive the world. Our research includes images, audio signals and acoustic images. The latter provide spatial audio information and are obtained from a planar array of microphones combining their raw audios with the beamforming algorithm. They better mimic human auditory systems, which cannot be replicated using just one microphone, not able alone to give spatial sound cues. However, as microphones arrays are not so widespread, we also study how to handle the missing spatialized audio modality at test time. As a solution, we propose to distill acoustic images content to audio features during the training in order to handle their absence at test time. This is done for supervised audio classification using the generalized distillation framework, which we also extend for self-supervised learning. Next, we devise a method for reconstructing acoustic images given a single microphone and an RGB frame. Therefore, in case we just dispose of a standard video, we are able to synthesize spatial audio, which is useful for many audio-visual tasks, including sound localization. Lastly, as another example of restoring one modality from available ones, we inpaint degraded images providing audio features, to reconstruct the missing region not only to be visually plausible but also semantically consistent with the related sound. This includes also cross-modal generation, in the limit case of completely missing or hidden visual modality: our method naturally deals with it, being able to generate images from sound. In summary we show how audio can help visual learning and vice versa, by transferring knowledge between the two modalities at training time, in order to distill, reconstruct, or restore the missing modality at test time

An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation

Speech enhancement and speech separation are two related tasks, whose purpose is to extract either one or more target speech signals, respectively, from a mixture of sounds generated by several sources. Traditionally, these tasks have been tackled using signal processing and machine learning techniques applied to the available acoustic signals. Since the visual aspect of speech is essentially unaffected by the acoustic environment, visual information from the target speakers, such as lip movements and facial expressions, has also been used for speech enhancement and speech separation systems. In order to efficiently fuse acoustic and visual information, researchers have exploited the flexibility of data-driven approaches, specifically deep learning, achieving strong performance. The ceaseless proposal of a large number of techniques to extract features and fuse multimodal information has highlighted the need for an overview that comprehensively describes and discusses audio-visual speech enhancement and separation based on deep learning. In this paper, we provide a systematic survey of this research topic, focusing on the main elements that characterise the systems in the literature: acoustic features; visual features; deep learning methods; fusion techniques; training targets and objective functions. In addition, we review deep-learning-based methods for speech reconstruction from silent videos and audio-visual sound source separation for non-speech signals, since these methods can be more or less directly applied to audio-visual speech enhancement and separation. Finally, we survey commonly employed audio-visual speech datasets, given their central role in the development of data-driven approaches, and evaluation methods, because they are generally used to compare different systems and determine their performance

A multimodal approach to blind source separation of moving sources

A novel multimodal approach is proposed to solve the problem of blind source separation (BSS) of moving sources. The challenge of BSS for moving sources is that the mixing filters are time varying; thus, the unmixing filters should also be time varying, which are difficult to calculate in real time. In the proposed approach, the visual modality is utilized to facilitate the separation for both stationary and moving sources. The movement of the sources is detected by a 3-D tracker based on video cameras. Positions and velocities of the sources are obtained from the 3-D tracker based on a Markov Chain Monte Carlo particle filter (MCMC-PF), which results in high sampling efficiency. The full BSS solution is formed by integrating a frequency domain blind source separation algorithm and beamforming: if the sources are identified as stationary for a certain minimum period, a frequency domain BSS algorithm is implemented with an initialization derived from the positions of the source signals. Once the sources are moving, a beamforming algorithm which requires no prior statistical knowledge is used to perform real time speech enhancement and provide separation of the sources. Experimental results confirm that by utilizing the visual modality, the proposed algorithm not only improves the performance of the BSS algorithm and mitigates the permutation problem for stationary sources, but also provides a good BSS performance for moving sources in a low reverberant environment

Bio-Inspired Modality Fusion for Active Speaker Detection

Human beings have developed fantastic abilities to integrate information from various sensory sources exploring their inherent complementarity. Perceptual capabilities are therefore heightened enabling, for instance, the well known "cocktail party" and McGurk effects, i.e. speech disambiguation from a panoply of sound signals. This fusion ability is also key in refining the perception of sound source location, as in distinguishing whose voice is being heard in a group conversation. Furthermore, Neuroscience has successfully identified the superior colliculus region in the brain as the one responsible for this modality fusion, with a handful of biological models having been proposed to approach its underlying neurophysiological process. Deriving inspiration from one of these models, this paper presents a methodology for effectively fusing correlated auditory and visual information for active speaker detection. Such an ability can have a wide range of applications, from teleconferencing systems to social robotics. The detection approach initially routes auditory and visual information through two specialized neural network structures. The resulting embeddings are fused via a novel layer based on the superior colliculus, whose topological structure emulates spatial neuron cross-mapping of unimodal perceptual fields. The validation process employed two publicly available datasets, with achieved results confirming and greatly surpassing initial expectations.Comment: Submitted to IEEE RA-L with IROS option, 202