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Music Information Retrieval: Recent Developments and Applications
We provide a survey of the field of Music Information Retrieval (MIR), in particular paying attention to latest developments, such as semantic auto-tagging and user-centric retrieval and…
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Current challenges and visions in music recommender systems research
TLDR
This article identifies and shed light on what it believes are the most pressing challenges MRS research is facing, from both academic and industry perspectives and details possible future directions and visions for the further evolution of the field.
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Polyphonic piano note transcription with recurrent neural networks
TLDR
A new approach for polyphonic piano note onset transcription based on a recurrent neural network to simultaneously detect the onsets and the pitches of the notes from spectral features and generalizes much better than existing systems.
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Evaluating the Online Capabilities of Onset Detection Methods
TLDR
A new onset detection method based on the common spectral flux and a new peak-picking method which outperforms traditional methods both online and offline and works with audio signals of various volume levels are proposed.
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The MediaEval 2013 Affect Task: Violent Scenes Detection
TLDR
The MediaEval 2014 Affect Task: Violent Scenes Detection, which is running for the fourth year, originates from a use case at Technicolorthat aims to help users find suitable contents from a movie database.
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The MediaEval 2015 Affective Impact of Movies Task
TLDR
Insight is provided on the use case, task challenges, data set and ground truth, task run requirements and evaluation metrics of the MediaEval 2015 Aective Impact of Movies Task.
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The LFM-1b Dataset for Music Retrieval and Recommendation
TLDR
The LFM-1b dataset of more than one billion music listening events created by more than 120,000 users of Last.fm is presented, with its substantial size and a wide range of additional user descriptors that reflect their music taste and consumption behavior.
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A survey of music similarity and recommendation from music context data
TLDR
An overview of methods for music similarity estimation and music recommendation based on music context data is given and the characteristics of the presented context-based measures are elaborates and discusses their strengths as well as their weaknesses.
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Local and global scaling reduce hubs in space
TLDR
Two classes of methods that try to symmetrize nearest neighbor relations are discussed and to what extent they can mitigate the negative effects of hubs and a real-world application where the methods are able to achieve significantly higher retrieval quality is presented.
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On Rhythm and General Music Similarity
TLDR
This paper proposes modifications to Fluctuation Patterns and shows that by combining these rhythmic descriptors with a timbral component, results for rhythm similarity computation are improved beyond the level obtained when using the rhythm descriptor component alone.
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