Improving the Performance of Online Neural Transducer Models

Having a sequence-to-sequence model which can operate in an online fashion is important for streaming applications such as Voice Search. Neural transducer is a streaming sequence-to-sequence model, but has shown a significant degradation in performance compared to non-streaming models such as Listen, Attend and Spell (LAS). In this paper, we present various improvements to NT. Specifically, we look at increasing the window over which NT computes attention, mainly by looking backwards in time so the model still remains online. In addition, we explore initializing a NT model from a LAS-trained model so that it is guided with a better alignment. Finally, we explore including stronger language models such as using wordpiece models, and applying an external LM during the beam search. On a Voice Search task, we find with these improvements we can get NT to match the performance of LAS

MSPlayer: Multi-Source and multi-Path LeverAged YoutubER

Online video streaming through mobile devices has become extremely popular nowadays. YouTube, for example, reported that the percentage of its traffic streaming to mobile devices has soared from 6% to more than 40% over the past two years. Moreover, people are constantly seeking to stream high quality video for better experience while often suffering from limited bandwidth. Thanks to the rapid deployment of content delivery networks (CDNs), popular videos are now replicated at different sites, and users can stream videos from close-by locations with low latencies. As mobile devices nowadays are equipped with multiple wireless interfaces (e.g., WiFi and 3G/4G), aggregating bandwidth for high definition video streaming has become possible. We propose a client-based video streaming solution, MSPlayer, that takes advantage of multiple video sources as well as multiple network paths through different interfaces. MSPlayer reduces start-up latency and provides high quality video streaming and robust data transport in mobile scenarios. We experimentally demonstrate our solution on a testbed and through the YouTube video service.Comment: accepted to ACM CoNEXT'1

Online multipath convolutional coding for real-time transmission

Most of multipath multimedia streaming proposals use Forward Error Correction (FEC) approach to protect from packet losses. However, FEC does not sustain well burst of losses even when packets from a given FEC block are spread over multiple paths. In this article, we propose an online multipath convolutional coding for real-time multipath streaming based on an on-the-fly coding scheme called Tetrys. We evaluate the benefits brought out by this coding scheme inside an existing FEC multipath load splitting proposal known as Encoded Multipath Streaming (EMS). We demonstrate that Tetrys consistently outperforms FEC in both uniform and burst losses with EMS scheme. We also propose a modification of the standard EMS algorithm that greatly improves the performance in terms of packet recovery. Finally, we analyze different spreading policies of the Tetrys redundancy traffic between available paths and observe that the longer propagation delay path should be preferably used to carry repair packets.Comment: Online multipath convolutional coding for real-time transmission (2012

An economic analysis of online streaming. How the music industry can generate revenues from cloud computing

This paper investigates the upcoming business model of online streaming services allowing music consumers either to subscribe to a service which provides free-of-charge access to streaming music and which is funded by advertising, or to pay a monthly flat fee in order to get ad-free access to the content of the service accompanied with additional benefits. By imposing a two-sided market model on the one hand combined with a direct transaction between the streaming service and its flat-rate subscribers on the other hand, the investigation shows that it can be highly profitable to launch a business which is free-of-charge for subscribers if advertising imposes a weak nuisance to music consumers. If this is the case, and by imposing an endogenously determined level of advertising which is provided by homogeneous advertisers, we find that a monopolistic streaming service increases the price for its flat-rate subscribers in order to stimulate free-of-charge demand and to capture higher revenues from advertisers. An extension of the model by illegal file-sharing shows that an increase in copyright enforcement shifts rents from music consumers to the monopolist. --Advertising media,Music industry,Online streaming,Piracy

Lowering the pirate flag: a TPB study of the factors influencing the intention to pay for movie streaming services

The launch of several movie streaming services has raised new questions about how online consumers deal with both legal and illegal options to obtain their desired products. This paper investigates the factors influencing consumers’ intentions to subscribe to online movie streaming services. These services have challenged the dramatic growth in their illegal counterpart in recent years. Taking the theory of planned behavior as a starting point, we extended existing models in the literature by incorporating factors that are specific to consumer behavior in this particular field. A quantitative survey was conducted for the Italian market, and structural equation modeling was used for data analysis. Attitudes, involvement with products, moral judgement and frequency of past behavior were found to be the most important factors in explaining the intention to pay for movie streaming services. The paper provides insights for policy makers and industry managers on the marketing communication strategies needed to minimize the risk of digital piracy