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Found 39 papers, 33 papers with code
Modéliser la perception des genres musicaux à travers différentes cultures à partir de ressources linguistiques (Modeling the Music Genre Perception across Language-Bound Cultures )
Nous résumons nos travaux de recherche, présentés à la conférence EMNLP 2020 et portant sur la modélisation de la perception des genres musicaux à travers différentes cultures, à partir de représentations sémantiques spécifiques à différentes langues.
Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation
In this paper, we introduce PISA (Psychology-Informed Session embedding using ACT-R), a session-level sequential recommender system that overcomes this limitation.
From Real to Cloned Singer Identification
In this paper, we investigate how singer identification methods could be used for such a task.
Improving Quotation Attribution with Fictional Character Embeddings
Humans naturally attribute utterances of direct speech to their speaker in literary works.
A Realistic Evaluation of LLMs for Quotation Attribution in Literary Texts: A Case Study of LLaMa3
In literary tasks, the performance of LLMs is often correlated to the degree of book memorization.
Detecting music deepfakes is easy but actually hard
In the face of a new era of generative models, the detection of artificially generated content has become a matter of utmost importance.
An Experimental Comparison Of Multi-view Self-supervised Methods For Music Tagging
In this study, we expand the scope of pretext tasks applied to music by investigating and comparing the performance of new self-supervised methods for music tagging.
Distinguishing Fictional Voices: a Study of Authorship Verification Models for Quotation Attribution
Recent approaches to automatically detect the speaker of an utterance of direct speech often disregard general information about characters in favor of local information found in the context, such as surrounding mentions of entities.
Ex2Vec: Characterizing Users and Items from the Mere Exposure Effect
Since previous research has shown that the magnitude of the effect depends on a number of interesting factors such as stimulus complexity and familiarity, leveraging this effect is a way to not only improve repeated recommendation but to gain a more in-depth understanding of both users and stimuli.
On the Consistency of Average Embeddings for Item Recommendation
A prevalent practice in recommender systems consists in averaging item embeddings to represent users or higher-level concepts in the same embedding space.
Zero-note samba: Self-supervised beat tracking
Supervised machine learning for music information retrieval requires a large annotated training set, and is thus an expensive and time-consuming process.
Of Spiky SVDs and Music Recommendation
The truncated singular value decomposition is a widely used methodology in music recommendation for direct similar-item retrieval or embedding musical items for downstream tasks.
Attention Mixtures for Time-Aware Sequential Recommendation
Transformers emerged as powerful methods for sequential recommendation.
A Human Subject Study of Named Entity Recognition (NER) in Conversational Music Recommendation Queries
We conducted a human subject study of named entity recognition on a noisy corpus of conversational music recommendation queries, with many irregular and novel named entities.
New Frontiers in Graph Autoencoders: Joint Community Detection and Link Prediction
It is still unclear to what extent one can improve CD with GAE and VGAE, especially in the absence of node features.
Discovery Dynamics: Leveraging Repeated Exposure for User and Music Characterization
Repetition in music consumption is a common phenomenon.
Learning Unsupervised Hierarchies of Audio Concepts
In this paper, we adapt concept learning to the realm of music, with its particularities.
Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Prediction
Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction.
Explainability in Music Recommender Systems
In this article, we discuss how explainability can be addressed in the context of MRSs.
Collaborative Filtering
Explainable artificial intelligence
+3
Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognition
The results show: auto-regressive language models as meta-learners can perform NET and NER fairly well especially for regular or seen names; name irregularity when often present for a certain entity type can become an effective exploitable cue; names with words foreign to the model have the most negative impact on results; the model seems to rely more on name than context cues in few-shot NER.
Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders
In this paper, we model this cold start similar artists ranking problem as a link prediction task in a directed and attributed graph, connecting artists to their top-k most similar neighbors and incorporating side musical information.
Hierarchical Latent Relation Modeling for Collaborative Metric Learning
Existing extensions of CML also either ignore the heterogeneity of user-item relations, i. e. that a user can simultaneously like very different items, or the latent item-item relations, i. e. that a user's preference for an item depends, not only on its intrinsic characteristics, but also on items they previously interacted with.
Singing Language Identification using a Deep Phonotactic Approach
Extensive works have tackled Language Identification (LID) in the speech domain, however their application to the singing voice trails and performances on Singing Language Identification (SLID) can be improved leveraging recent progresses made in other singing related tasks.
Towards Rigorous Interpretations: a Formalisation of Feature Attribution
Feature attribution is often loosely presented as the process of selecting a subset of relevant features as a rationale of a prediction.
Modeling the Music Genre Perception across Language-Bound Cultures
The music genre perception expressed through human annotations of artists or albums varies significantly across language-bound cultures.
Cultural Vocal Bursts Intensity Prediction
Information Retrieval
+2
Multilingual Music Genre Embeddings for Effective Cross-Lingual Music Item Annotation
However, without a parallel corpus, previous solutions could not handle tag systems in other languages, being limited to the English-language only.
Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction
Explaining recommendations enables users to understand whether recommended items are relevant to their needs and has been shown to increase their trust in the system.
Muzeeglot : annotation multilingue et multi-sources d'entit\'es musicales \`a partir de repr\'esentations de genres musicaux (Muzeeglot : cross-lingual multi-source music item annotation from music genre embeddings)
Au sein de cette d{\'e}monstration, nous pr{\'e}sentons Muzeeglot, une interface web permettant de visualiser des espaces de repr{\'e}sentations de genres musicaux provenant de sources vari{\'e}es et de langues diff{\'e}rentes.
FastGAE: Scalable Graph Autoencoders with Stochastic Subgraph Decoding
Graph autoencoders (AE) and variational autoencoders (VAE) are powerful node embedding methods, but suffer from scalability issues.
Simple and Effective Graph Autoencoders with One-Hop Linear Models
Over the last few years, graph autoencoders (AE) and variational autoencoders (VAE) emerged as powerful node embedding methods, with promising performances on challenging tasks such as link prediction and node clustering.
Spleeter: A Fast And State-of-the Art Music Source Separation Tool With Pre-trained Models
We present and release a new tool for music source separation with pre-trained models called Spleeter. Spleeter was designed with ease of use, separation performance and speed in mind.
Ranked #19 on
Music Source Separation
on MUSDB18
(using extra training data)
Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks
Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods, with promising performances on challenging tasks such as link prediction and node clustering.
Improving Collaborative Metric Learning with Efficient Negative Sampling
Distance metric learning based on triplet loss has been applied with success in a wide range of applications such as face recognition, image retrieval, speaker change detection and recently recommendation with the CML model.
Leveraging Knowledge Bases And Parallel Annotations For Music Genre Translation
Here, we choose a new angle for the genre study by seeking to predict what would be the genres of musical items in a target tag system, knowing the genres assigned to them within source tag systems.
Gravity-Inspired Graph Autoencoders for Directed Link Prediction
Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods.
A Degeneracy Framework for Scalable Graph Autoencoders
In this paper, we present a general framework to scale graph autoencoders (AE) and graph variational autoencoders (VAE).
Disambiguating Music Artists at Scale with Audio Metric Learning
We address the problem of disambiguating large scale catalogs through the definition of an unknown artist clustering task.
Music Mood Detection Based On Audio And Lyrics With Deep Neural Net
We consider the task of multimodal music mood prediction based on the audio signal and the lyrics of a track.
