432 research outputs found
Processing and Linking Audio Events in Large Multimedia Archives: The EU inEvent Project
In the inEvent EU project [1], we aim at structuring, retrieving, and sharing large archives of networked, and dynamically changing, multimedia recordings, mainly consisting of meetings, videoconferences, and lectures. More specifically, we are developing an integrated system that performs audiovisual processing of multimedia recordings, and labels them in terms of interconnected “hyper-events ” (a notion inspired from hyper-texts). Each hyper-event is composed of simpler facets, including audio-video recordings and metadata, which are then easier to search, retrieve and share. In the present paper, we mainly cover the audio processing aspects of the system, including speech recognition, speaker diarization and linking (across recordings), the use of these features for hyper-event indexing and recommendation, and the search portal. We present initial results for feature extraction from lecture recordings using the TED talks. Index Terms: Networked multimedia events; audio processing: speech recognition; speaker diarization and linking; multimedia indexing and searching; hyper-events. 1
CoupleNet: Paying Attention to Couples with Coupled Attention for Relationship Recommendation
Dating and romantic relationships not only play a huge role in our personal
lives but also collectively influence and shape society. Today, many romantic
partnerships originate from the Internet, signifying the importance of
technology and the web in modern dating. In this paper, we present a text-based
computational approach for estimating the relationship compatibility of two
users on social media. Unlike many previous works that propose reciprocal
recommender systems for online dating websites, we devise a distant supervision
heuristic to obtain real world couples from social platforms such as Twitter.
Our approach, the CoupleNet is an end-to-end deep learning based estimator that
analyzes the social profiles of two users and subsequently performs a
similarity match between the users. Intuitively, our approach performs both
user profiling and match-making within a unified end-to-end framework.
CoupleNet utilizes hierarchical recurrent neural models for learning
representations of user profiles and subsequently coupled attention mechanisms
to fuse information aggregated from two users. To the best of our knowledge,
our approach is the first data-driven deep learning approach for our novel
relationship recommendation problem. We benchmark our CoupleNet against several
machine learning and deep learning baselines. Experimental results show that
our approach outperforms all approaches significantly in terms of precision.
Qualitative analysis shows that our model is capable of also producing
explainable results to users.Comment: Accepted at ICWSM 201
- …