The matching of the soundtrack in a movie or a video can have an enormous influence in the message being con- veyed and its impact, in the sense of involvement and engagement, and ultimately in their aesthetic and entertainment qualities. Art is often associated with creativity, implying the presence of inspiration, originality and appropriateness. Evolutionary systems provides us with the novelty, showing us new and subtly different solutions in every generation, possibly stimulating the creativity of the human using the system. In this paper, we present Genetic Soundtracks, an evolutionary approach to the creative matching of audio to a video. It analyzes both media to extract features based on their content, and adopts genetic algorithms, with the purpose of truncating, combining and adjusting audio clips, to align and match them with the video scenes. Index Terms— Genetic algorithms, multimedia, entertainment, feature extraction, audio & video signal processing, video editing
In Synesthetic Video @cite_7 , the authors explored the relation of visual and auditory properties to experience video in cross-sensorial modes, resulting in ways to hear its colours (synthesised, not matching of existing audio) and to influence its visual properties with sound and music, through user interaction or ambient influence. The motivations behind this work were accessibility, enriching users' experiences, and stimulating and supporting creativity. @cite_3 is performed automatic and semi-automatic selection and alignment of video segments to music. The objective of the proposed method is to create a suitable video track for the given soundtrack, which is the opposite of our work. The process is based on the detection of audio and video changes, plus camera motion and exposure, to help determine suitability between the video and audio tracks. Deterministic methods are proposed for the alignment of audio and video, such as best-first search.
@cite_3 @cite_7
2001555052 2065274193 2770520232 2793262076
In this paper we present Synesthetic Video, an interactive video that allows to experience video in cross-sensorial ways, to hear its colors and to influence its visual properties with sound and music, through user interaction or ambient influence. Our main motivations include accessibility, enriching users experiences, stimulating and supporting users creativity, and to learn more about synesthesia and how videos can influence and be influenced by users and the ambient, at the crossroads of art, science and technology. In this paper, we propose a novel method that exploits correlation between audio-visual dynamics of a video to segment and localize objects that are the dominant source of audio. Our approach consists of a two-step spatiotemporal segmentation mechanism that relies on velocity and acceleration of moving objects as visual features. Each frame of the video is segmented into regions based on motion and appearance cues using the QuickShift algorithm, which are then clustered over time using K-means, so as to obtain a spatiotemporal video segmentation. The video is represented by motion features computed over individual segments. The Mel-Frequency Cepstral Coefficients (MFCC) of the audio signal, and their first order derivatives are exploited to represent audio. The proposed framework assumes there is a non-trivial correlation between these audio features and the velocity and acceleration of the moving and sounding objects. The canonical correlation analysis (CCA) is utilized to identify the moving objects which are most correlated to the audio signal. In addition to moving-sounding object identification, the same framework is also exploited to solve the problem of audio-video synchronization, and is used to aid interactive segmentation. We evaluate the performance of our proposed method on challenging videos. Our experiments demonstrate significant increase in performance over the state-of-the-art both qualitatively and quantitatively, and validate the feasibility and superiority of our approach. Video caption refers to generating a descriptive sentence for a specific short video clip automatically, which has achieved remarkable success recently. However, most of the existing methods focus more on visual information while ignoring the synchronized audio cues. We propose three multimodal deep fusion strategies to maximize the benefits of visual-audio resonance information. The first one explores the impact on cross-modalities feature fusion from low to high order. The second establishes the visual-audio short-term dependency by sharing weights of corresponding front-end networks. The third extends the temporal dependency to long-term through sharing multimodal memory across visual and audio modalities. Extensive experiments have validated the effectiveness of our three cross-modalities fusion strategies on two benchmark datasets, including Microsoft Research Video to Text (MSRVTT) and Microsoft Video Description (MSVD). It is worth mentioning that sharing weight can coordinate visual-audio feature fusion effectively and achieve the state-of-art performance on both BELU and METEOR metrics. Furthermore, we first propose a dynamic multimodal feature fusion framework to deal with the part modalities missing case. Experimental results demonstrate that even in the audio absence mode, we can still obtain comparable results with the aid of the additional audio modality inference module. In this paper, we introduce a novel problem of audio-visual event localization in unconstrained videos. We define an audio-visual event as an event that is both visible and audible in a video segment. We collect an Audio-Visual Event(AVE) dataset to systemically investigate three temporal localization tasks: supervised and weakly-supervised audio-visual event localization, and cross-modality localization. We develop an audio-guided visual attention mechanism to explore audio-visual correlations, propose a dual multimodal residual network (DMRN) to fuse information over the two modalities, and introduce an audio-visual distance learning network to handle the cross-modality localization. Our experiments support the following findings: joint modeling of auditory and visual modalities outperforms independent modeling, the learned attention can capture semantics of sounding objects, temporal alignment is important for audio-visual fusion, the proposed DMRN is effective in fusing audio-visual features, and strong correlations between the two modalities enable cross-modality localization.