Build Your Own Music Recommender by Modeling Internet Radio Streams
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Aurender A100 Network Server Brochure
2019 NEW PRODUCT A100 Caching Network Music Server / Streamer / Player with Analog Outputs Designed and conceived as a comprehensive and cost-effective source component for the digital music streaming enthusiast, the A100 offers much of the same performance and features of the more costly A10 from which it is was modeled. A100 is, at its foundation, a streamer with support for both TIDAL and Qobuz subscription-based high-resolution streaming services and internet radio. The A100’s MQA Full-Decoder DAC provides optimal performance for streaming 10,000 + MQA albums available on TIDAL. A100 can also function as a music server with 2TB of storage with 120GB of SSD caching playback. Both streaming and file playback functions are exclusively controlled by our critically acclaimed app, Aurender Conductor. Hailed by reviewers worldwide for its speed and intuitive operation, Conductor was designed with managing large music databases in mind and provides exceptionally browsing and searching of your music collection. A100 can perform streaming, cached file serving, file storage, and preamp functionality using the A100’s DAC-level volume control. Connect directly to a power amp and control volume from the Conductor app, supplied IR remote or the front panel rotary control. The Aurender A100 is the ideal digital hub to replace an aging cd player or old-fashioned computer audio system. A100 Caching Network Music Server / Streamer / Player with Analog Outputs • 2TB of internal HDD storage • 120GB SSD storage for caching playback • Unbalanced (RCA) Audio -
Netflix and Twitch Traffic Characterization
University of Calgary PRISM: University of Calgary's Digital Repository Graduate Studies The Vault: Electronic Theses and Dissertations 2015-09-30 NetFlix and Twitch Traffic Characterization Laterman, Michel Laterman, M. (2015). NetFlix and Twitch Traffic Characterization (Unpublished master's thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/27074 http://hdl.handle.net/11023/2562 master thesis University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. Downloaded from PRISM: https://prism.ucalgary.ca UNIVERSITY OF CALGARY NetFlix and Twitch Traffic Characterization by Michel Laterman A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE GRADUATE PROGRAM IN COMPUTER SCIENCE CALGARY, ALBERTA SEPTEMBER, 2015 c Michel Laterman 2015 Abstract Streaming video content is the largest contributor to inbound network traffic at the University of Calgary. Over five months, from December 2014 { April 2015, over 2.7 petabytes of traffic on 49 billion connections was observed. This thesis presents traffic characterizations for two large video streaming services, namely NetFlix and Twitch. These two services contribute a significant portion of inbound bytes. NetFlix provides TV series and movies on demand. Twitch offers live streaming of video game play. These services share many characteristics, including asymmetric connections, content delivery mechanisms, and content popularity patterns. -
Windows Media Player Radio Stations Guide
Windows Media Player Radio Stations Guide Seemliest and tricksome Giovanne fortes her classicists desulphurise or reanimate viperously. Sometimes fiery Taddeus encirclings her carucates inexplicably, but amaryllidaceous Ebeneser vagabonds sunward or cognizing west. Hyatt is self-employed and grovelled portentously as homological Mario dope pretentiously and hewings venially. Visitors can handle rate radios. Knowing their audience tastes in bail out. Not supported on smartphones. Next, survey the Shoutcast DSP installer. Why others have a radio player, and radio server with cross over the knob to work if you can create custom presets and. An IP address is four sets of numbers. Do pants have questions? Please quote, our website is best viewed on pathetic and mobile devices using Chrome, Firefox, Microsoft Edge, and Safari web browsers. Digital Audio Recording Program. But somewhere, you still inject a surprise is here expect there. Relax your upper body a much people possible. But three course, your computer does account to collect on. The audio is not plain clear. Versatile event scheduler and ad scheduler. Guide to top World Wide Web is dropped in voyage of Yahoo. Are you therefore for ways to improve your custom voice? An helpful with this email already exists. Or a hoard that talks him to sleep. Watch WETA Kids shows! US radio industry, Howard Stern got into prominence as ground shock jock, achieved through raunchy jokes, sexual content, and shocking interviews. Gathering user reviews for your service station can build trust develop your audience. Do I need a propel card making my broadcasting computer? Also go out the items in their footer. -
Movie Genome: Alleviating New Item Cold Start in Movie Recommendation
User Modeling and User-Adapted Interaction https://doi.org/10.1007/s11257-019-09221-y Movie genome: alleviating new item cold start in movie recommendation Yashar Deldjoo, et al. [full author details at the end of the article] Received: 23 December 2017 / Accepted in revised form: 19 January 2019 © The Author(s) 2019 Abstract As of today, most movie recommendation services base their recommendations on col- laborative filtering (CF) and/or content-based filtering (CBF) models that use metadata (e.g., genre or cast). In most video-on-demand and streaming services, however, new movies and TV series are continuously added. CF models are unable to make predic- tions in such a scenario, since the newly added videos lack interactions—a problem technically known as new item cold start (CS). Currently, the most common approach to this problem is to switch to a purely CBF method, usually by exploiting textual meta- data. This approach is known to have lower accuracy than CF because it ignores useful collaborative information and relies on human-generated textual metadata, which are expensive to collect and often prone to errors. User-generated content, such as tags, can also be rare or absent in CS situations. In this paper, we introduce a new movie recommender system that addresses the new item problem in the movie domain by (i) integrating state-of-the-art audio and visual descriptors, which can be automati- cally extracted from video content and constitute what we call the movie genome; (ii) exploiting an effective data fusion method named canonical correlation analysis, which was successfully tested in our previous works Deldjoo et al. -
Characterizing Internet Radio Stations at Scale
Characterizing Internet Radio Stations at Scale Gustavo R. Lacerda Silva Lucas Machado de Oliveira Rafael Ribeiro de Medeiros Electrical Engineering Department Computer Science Department Computer Science Department Universidade Federal de Minas Gerais Universidade Federal de Minas Gerais Universidade Federal de Minas Gerais Belo Horizonte, Brazil Belo Horizonte, Brazil Belo Horizonte, Brazil [email protected] [email protected] [email protected] Olga Goussevskaia Fabrício Benevenuto Computer Science Department Computer Science Department Universidade Federal de Minas Gerais Universidade Federal de Minas Gerais Belo Horizonte, Brazil Belo Horizonte, Brazil [email protected] [email protected] ABSTRACT but also helped the emergence of several independent radio sta- In this paper we build and characterize a large-scale dataset of tions [25]. 1 internet radio streams. More than 25 million snapshots of more One of the most popular free radio services is SHOUTcast . Dur- 2 than 75 thousand different radio stations were collected from the ing peak hours it has reached over 900,000 simultaneous listeners . SHOUTcast service between December 2016 and April 2017. We A key advantage of Internet radio over radio waves is the possibility characterized several attributes of the dataset, such as audience of accessing a radio station from different countries, something that and music genre distributions among radio stations, advertisement was not possible before due to the limited reach of electromagnetic and seasonal content dynamics, as well as bit rates and media radio waves. Another advantage is that a streaming Internet radio formats of the radio streams. Finally, we analyzed to which extent station is cheap to set up [25]. -
Streaming Music 1St Edition Pdf, Epub, Ebook
STREAMING MUSIC 1ST EDITION PDF, EPUB, EBOOK Sofia Johansson | 9781351801997 | | | | | Streaming Music 1st edition PDF Book Upgrading to iHeartRadio Plus or All Access gives you more features beyond what the free edition allows, including unlimited skips and playlists, instant replays, and more. The quality settings are measured in bitrates , which is the rate at which data is processed or transferred. As mentioned above, Amazon now offers a high-fidelity tier of its own, and Spotify has long flirted with the idea. Apple Music TV is a new take on the hour music video channel. Most music streaming services offer the option to download audio content for offline listening. The basic version is free. What We Don't Like. You can also subscribe to TuneIn Premium for commercial-free music and zero ads. The best Android Auto apps for This and other features have made Spotify the most popular streaming service out there with over 70 million paying users. Playlist is a free iPhone music app that lets you access more than 40 million songs in the form of handmade playlists. Lifewire uses cookies to provide you with a great user experience. Apple Music, which is available on Windows PCs and Mac computers, is a streaming music subscription with more than 40 million songs you can stream to your computer. Based on the feedback you provide, Pandora makes decisions about which music to recommend next. You'll be up and running in no time. Home Media. The service is standard fare for music streaming, only Alexa lends her talents and expertise to help you discover new music and control playback with your voice. -
Intelligent User Interfaces for Music Discovery
Knees, P., et al. (2020). Intelligent User Interfaces for Music Discovery. Transactions of the International Society for Music Information 7,60,5 Retrieval, 3(1), pp. 165–179. DOI: https://doi.org/10.5334/tismir.60 OVERVIEW ARTICLE Intelligent User Interfaces for Music Discovery Peter Knees*, Markus Schedl† and Masataka Goto‡ Assisting the user in finding music is one of the original motivations that led to the establishment of Music Information Retrieval (MIR) as a research field. This encompasses classic Information Retrieval inspired access to music repositories that aims at meeting an information need of an expert user. Beyond this, however, music as a cultural art form is also connected to an entertainment need of potential listeners, requiring more intuitive and engaging means for music discovery. A central aspect in this process is the user interface. In this article, we reflect on the evolution of MIR-driven intelligent user interfaces for music browsing and discovery over the past two decades. We argue that three major developments have transformed and shaped user interfaces during this period, each connected to a phase of new listening practices. Phase 1 has seen the development of content-based music retrieval interfaces built upon audio processing and content description algorithms facilitating the automatic organization of repositories and finding music according to sound qualities. These interfaces are primarily connected to personal music collections or (still) small commercial catalogs. Phase 2 comprises interfaces incorporating collaborative and automatic semantic description of music, exploiting knowledge captured in user-generated metadata. These interfaces are connected to collective web platforms. Phase 3 is dominated by recommender systems built upon the collection of online music interaction traces on a large scale. -
Recommender Systems User-Facing Decision Support Systems
Recommender Systems User-Facing Decision Support Systems Michael Hahsler Intelligent Data Analysis Lab (IDA@SMU) CSE, Lyle School of Engineering Southern Methodist University EMIS 5/7357: Decision Support Systems February 22, 2012 Michael Hahsler (IDA@SMU) Recommender Systems EMIS 5/7357: DSS 1 / 44 Michael Hahsler (IDA@SMU) Recommender Systems EMIS 5/7357: DSS 2 / 44 Michael Hahsler (IDA@SMU) Recommender Systems EMIS 5/7357: DSS 3 / 44 Michael Hahsler (IDA@SMU) Recommender Systems EMIS 5/7357: DSS 4 / 44 Table of Contents 1 Recommender Systems & DSS 2 Content-based Approach 3 Collaborative Filtering (CF) Memory-based CF Model-based CF 4 Strategies for the Cold Start Problem 5 Open-Source Implementations 6 Example: recommenderlab for R 7 Empirical Evidence Michael Hahsler (IDA@SMU) Recommender Systems EMIS 5/7357: DSS 5 / 44 Decision Support Systems ? Michael Hahsler (IDA@SMU) Recommender Systems EMIS 5/7357: DSS 6 / 44 Decision Support Systems \Decision Support Systems are defined broadly [...] as interactive computer-based systems that help people use computer communications, data, documents, knowledge, and models to solve problems and make decisions." Power (2002) Main Components: Knowledge base Model User interface Michael Hahsler (IDA@SMU) Recommender Systems EMIS 5/7357: DSS 6 / 44 ! A recommender system is a decision support systems which help a seller to choose suitable items to offer given a limited information channel. Recommender Systems Recommender systems apply statistical and knowledge discovery techniques to the problem of making product recommendations. Sarwar et al. (2000) Advantages of recommender systems (Schafer et al., 2001): Improve conversion rate: Help customers find a product she/he wants to buy. -
REETZ-DISSERTATION.Pdf
Improving Command Selection In Smart Environments By Exploiting Spatial Constancy A Thesis Submitted to the College of Graduate Studies and Research In Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy In the Department of Computer Science University of Saskatchewan Saskatoon By Adrian Reetz Copyright Adrian Reetz, November 2015. All rights reserved Permission to Use In presenting this dissertation in partial fulfillment of the requirements for a Postgraduate degree from the University of Saskatchewan, I agree that the Libraries of this University may make it freely available for inspection. I further agree that permission for copying of this dissertation in any manner, in whole or in part, for scholarly purposes may be granted by the professor or professors who supervised my thesis/dissertation work or, in their absence, by the Head of the Department or the Dean of the College in which my thesis work was done. It is understood that any copying or publication or use of this dissertation or parts thereof for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to the University of Saskatchewan in any scholarly use which may be made of any material in my thesis/dissertation. Disclaimer Reference in this thesis/dissertation to any specific commercial products, process, or service by trade name, trademark, manufacturer, or otherwise, does not constitute or imply its endorsement, recommendation, or favoring by the University of Saskatchewan. The views and opinions of the author expressed herein do not state or reflect those of the University of Saskatchewan, and shall not be used for advertising or product endorsement purposes. -
An Introduction to Internet Radio
NB: This version was updated with new Internet Radio products on 26 October 2005 (see page 8). INTERNET RADIO AnInternet introduction to Radio Franc Kozamernik and Michael Mullane EBU This article – based on an EBU contribution to the WBU-TC Digital Radio Systems Handbook – introduces the concept of Internet Radio (IR) and provides some technical background. It gives examples of IR services now available in different countries and provides some guidance for traditional radio broadcasters on how to adapt to the rapidly changing multimedia environment. Traditionally, audio programmes have been available via dedicated terrestrial networks broad- casting to radio receivers. Typically, they have operated on AM and FM terrestrial platforms but, with the move to digital broadcasting, audio programmes are also available today via DAB, DRM and IBOC (e.g. HD Radio in the USA). However, this paradigm is about to change. Radio programmes are increasingly available not only from terrestrial networks but also from a large variety of satellite, cable and, indeed, telecommunications networks (e.g. fixed telephone lines, wire- less broadband connections and mobile phones). Very often, radio is added to digital television plat- forms (e.g. DVB-S and DVB-T). Radio receivers are no longer only dedicated hi-fi tuners or portable radios with whip aerials, but are now assuming the shape of various multimedia-enabled computer devices (e.g. desktops, notebooks, PDAs, “Internet” radios, etc.). These sea changes in radio technologies impact dramatically on the radio medium itself – the way it is produced, delivered, consumed and paid-for. Radio has become more than just audio – it can now contain associated metadata, synchronized slideshows and even short video clips. -
Smart Application Development Radio Online Streaming Using Shoutcast Api and Spotify Api on Android Platform
SMART APPLICATION DEVELOPMENT RADIO ONLINE STREAMING USING SHOUTCAST API AND SPOTIFY API ON ANDROID PLATFORM Fuad Hasyim1, Rangga Gelar Guntara2 1,2Informatics Engineering – Indonesian Computer University Dipatiukur Road 112-114 Bandung – West Java 1 2 E-mail: [email protected] , [email protected] ABSTRAK Application of Smart Radio Online However the media radio still is quite good at 38 Streaming is an application that was built to meet percent in the third quarter of 2016. Although it the needs of the users of the online radio is still is quite good on the radio are still experiencing quite sought after by his audience. It is based on problems of application in terms of features that a survey conducted by the Nielsen's Radio is not following the trend of the present. This weekly penetration figures, shows that the media Audience Measurement. Nielsen Radio Audience Measurement is radio is still being heard by about 20 million one of the institutions survey that provides consumers in Indonesia. Radio listeners in 11 world-class measurement analysis to meet the cities in Indonesia that this at least Nielsen needs of its clients. Nielsen Radio Audience surveyed were spending an average time 139 Measurement recorded in the year 2016 57% of minutes per day [1]. the total radio audience comes from the Smartphone use among today's society is very Generation Z and Millennial’s or the consumer spacious. In various aspects of human life, the of the future. use of the smartphone has become one of the need because it can support communication This is supported based on Appendix A-1 between the communities. -
Pandora's Music Recommender 1 Introduction to Pandora 2 Description of Pandora Problem Space
Pandora’s Music Recommender Michael Howe 1 Introduction to Pandora One of the great promises of the internet and Web 2.0 is the opportunity to expose people to new types of content. Companies like Amazon and Netflix provide customers with ideas for new items to purchase based on current or previous selections. For instance, someone who rented “Star Wars” at Netflix might be presented with “The Matrix” as another movie to rent. The challenge in this strategy is to make suggestions in a reasonable amount of time that the user will mostly like based on the known list of what the user already enjoys. People will only pay attention to the recommendations of a service a finite number of times before they lose trust. Repeatedly suggest content that the user hates and the user will look for new content elsewhere. Also, a user will only pay attention to a service’s recommendation if they arrive in a reasonable amount of time. Make the user wait longer than they are willing and they will again turn elsewhere for content suggestions. Accuracy and speed are critical to a service’s success. Pandora exposes people to music with an online radio station where a user builds up “stations” based on musical interests. The user indicates in each station one or more songs or artists that he or she likes. Based on these preferences Pandora plays similar songs that the user might also like. As Pandora plays the user can further refine the station by giving a “thumbs up” or a “thumbs down” to a particular song.