Understanding
Creative media
and how people relate to it
Deezer Research is a team of scientists and
engineers specialized in Music and Audio Analysis,
Natural Language Understanding,
and Recommender Systems.
Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation
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STONE: Self-supervised Tonality Estimator
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From Real to Cloned Singer Identification
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Do Recommender Systems Promote Local Music? A Reproducibility Study Using Music Streaming Data
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An Experimental Comparison of Multi-View Self-Supervised Methods for Music Tagging
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Let’s Get It Started: Fostering the Discoverability of New Releases on Deezer
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Modeling Activity-Driven Music Listening with PACE
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Depict or Discern? Fingerprinting Musical Taste from Explicit Preferences
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Understanding Individual and Collective Diversity of Cultural Consumption through Large-Scale Music Listening Events
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Track Mix Generation on Music Streaming Services using Transformers
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On the Consistency of Average Embeddings for Item Recommendation
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Ex2Vec: Characterizing Users and Items from the Mere Exposure Effect
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Of Spiky SVDs and Music Recommendation
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Zero-Note Samba: Self-Supervised Beat Tracking
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Automatic Annotation of Direct Speech in Written French Narratives
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A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services
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Attention Mixtures for Time-Aware Sequential Recommendation
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Pauzee : Pauses Prediction in Text Reading
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A Human Subject Study of Named Entity Recognition (NER) in Conversational Music Recommendation Queries
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Data-Efficient Playlist Captioning With Musical and Linguistic Knowledge
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New Frontiers in Graph Learning: Joint Community Detection and Link Prediction
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Learning Unsupervised Hierarchies of Audio Concepts
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Exploiting Device and Audio Data to Tag Music with User-Aware Listening Contexts
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Network Analyses for Cross-Cultural Music Popularity
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Flow Moods: Recommending Music by Moods on Deezer
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Discovery Dynamics: Leveraging Repeated Exposure for User and Music Characterization
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Improving Transformers with Probabilistic Attention Keys
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Navigational, Informational or Punk-Rock? An Exploration of Search Intent in the Musical Domain
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Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Prediction
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Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognition
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FlowVocoder: A small Footprint Neural Vocoder based Normalizing Flow for Speech Synthesis
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Uplifting Interviews in Social Science with Individual Data Visualization: the Case of Music Listening
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Topic Modeling on Podcast Short-Text Metadata
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Explainability in Music Recommender Systems
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The Words Remain the Same: Cover Detection With Lyrics Transcription
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User-centered evaluation of lyrics to audio alignment
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Are Metal Fans Angrier than Jazz Fans? A Genre-Wise Exploration of the Emotional Language of Music Listeners on Reddit
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Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders
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Hierarchical Latent Relation Modeling for Collaborative Metric Learning
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Follow the Guides: Disentangling Human and Algorithmic Curation in Online Music Consumption
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A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps
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Visualyre: Multimodal Visualization of Lyrics
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Towards Rigorous Interpretations: a Formalisation of Feature Attribution
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Singing Language Identification using a Deep Phonotactic Approach
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FastGAE: Scalable Graph Autoencoders with Stochastic Subgraph Decoding
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The WASABI Dataset: Cultural, Lyrics and Audio Analysis Metadata about 2 Million Popular Commercially Released Songs
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Many-to-Many Voice Conversion based Feature Disentanglement using Variational Autoencoder
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Modeling the Music Genre Perception across Language-Bound Cultures
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Multilingual Lyrics-to-Audio Alignment
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Should We Consider the Users in Contextual Music Auto-Tagging Models?
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Multilingual Music Genre Embeddings for Effective Cross-Lingual Music Item Annotation
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Prediction of User Listening Contexts for Music Playlists
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Carousel Personalization in Music Streaming Apps with Contextual Bandits
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Making Neural Networks Interpretable with Attribution
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Spleeter: a Fast and Efficient Music Source Separation Tool with Pre-Trained Models
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Simple and Effective Graph Autoencoders with One-Hop Linear Models
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Muzeeglot: a Web Interface for Visualizing Multilingual Music Genre Embedding Spaces
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Confidence-Based Weighted Loss for Multi-label Classification with Missing Labels
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Audio-Based Detection of Explicit Content in Music
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Audio-Based Auto-Tagging with Contextual Tags for Music
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Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks
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Gravity-Inspired Graph Autoencoders for Directed Link Prediction
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Leveraging Knowledge Bases and Parallel Annotations for Music Genre Translation
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A Degeneracy Framework for Scalable Graph Autoencoders
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Improving Collaborative Metric Learning with Efficient Negative Sampling
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Singing Voice Separation: a Study on Training Data
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Time Warp Invariant Dictionary Learning for Time Series Clustering: Application to Music Data Stream Analysis
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Word2Vec applied to Recommendation: Hyperparameters Matter
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Disambiguating Music Artists at Scale with Audio Metric Learning
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Audio Based Disambiguation of Music Genre Tags
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Music Mood Detection based on Audio and Lyrics with Deep Neural Net
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Large-Scale Cover Song Detection in Digital Music Libraries Using Metadata, Lyrics and Audio Features
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WASABI: a Two Million Song Database Project with Audio and Cultural Metadata plus WebAudio enhanced Client Applications
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Codec Independent Lossy Audio Compression Detection
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