His primary areas of investigation include Music information retrieval, Information retrieval, World Wide Web, Recommender system and Audio signal. As a part of the same scientific family, Markus Schedl mostly works in the field of Music information retrieval, focusing on Cognitive models of information retrieval and, on occasion, Music and artificial intelligence and User modeling. His Information retrieval study incorporates themes from Context, Web application, Web page and Active listening.
His World Wide Web research is multidisciplinary, incorporating perspectives in Pop music automation and Similarity. His Recommender system research includes elements of Knowledge management, Social media and Big Five personality traits, Personality. His Audio signal study combines topics from a wide range of disciplines, such as Recurrent neural network and Artificial intelligence.
His primary areas of study are Information retrieval, Recommender system, Multimedia, Music information retrieval and World Wide Web. His biological study spans a wide range of topics, including Context, Web page, Similarity, Artificial intelligence and Popularity. Markus Schedl has researched Recommender system in several fields, including Big Five personality traits, Personality, User modeling and Active listening.
His studies in Multimedia integrate themes in fields like Classical music and Music listening. His Music information retrieval research includes elements of Human–computer information retrieval, Search engine indexing and Audio signal. His work on Social media, Microblogging and Personalization as part of general World Wide Web study is frequently linked to Information system, bridging the gap between disciplines.
Markus Schedl mainly investigates Recommender system, Active listening, Information retrieval, Collaborative filtering and Artificial intelligence. His Recommender system research includes themes of Quality, Context, Metadata, User modeling and Popularity. His research in Active listening intersects with topics in Applied psychology and Music information retrieval.
His Information retrieval research is multidisciplinary, incorporating perspectives in Profiling and Benchmark. His Collaborative filtering study integrates concerns from other disciplines, such as Artificial neural network, Feature extraction and Feature vector. His Deep learning and Ranking study in the realm of Artificial intelligence connects with subjects such as Baseline and Perspective.
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Music Information Retrieval: Recent Developments and Applications
Markus Schedl;Emilia Gómez;Julián Urbano.
Current challenges and visions in music recommender systems research
Markus Schedl;Hamed Zamani;Ching-Wei Chen;Yashar Deldjoo.
International Journal of Multimedia Information Retrieval (2018)
Polyphonic piano note transcription with recurrent neural networks
Sebastian Bock;Markus Schedl.
international conference on acoustics, speech, and signal processing (2012)
The neglected user in music information retrieval research
Markus Schedl;Arthur Flexer;Julián Urbano.
intelligent information systems (2013)
Evaluating the Online Capabilities of Onset Detection Methods.
Sebastian Böck;Florian Krebs;Markus Schedl.
international symposium/conference on music information retrieval (2012)
A survey of music similarity and recommendation from music context data
Peter Knees;Markus Schedl.
ACM Transactions on Multimedia Computing, Communications, and Applications (2013)
The LFM-1b Dataset for Music Retrieval and Recommendation
international conference on multimedia retrieval (2016)
An innovative three-dimensional user interface for exploring music collections enriched
Peter Knees;Markus Schedl;Tim Pohle;Gerhard Widmer.
acm multimedia (2006)
A music search engine built upon audio-based and web-based similarity measures
Peter Knees;Tim Pohle;Markus Schedl;Gerhard Widmer.
international acm sigir conference on research and development in information retrieval (2007)
Fusing Social Media Cues: Personality Prediction from Twitter and Instagram
Marcin Skowron;Marko Tkalčič;Bruce Ferwerda;Markus Schedl.
the web conference (2016)
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