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Musical dynamics form a core part of expressive singing voice performances. However, automatic analysis of musical dynamics for singing voice has received limited attention partly due to the scarcity of suitable datasets and a lack of clear evaluatio
Externí odkaz:
http://arxiv.org/abs/2410.20540
Current version identification (VI) datasets often lack sufficient size and musical diversity to train robust neural networks (NNs). Additionally, their non-representative clique size distributions prevent realistic system evaluations. To address the
Externí odkaz:
http://arxiv.org/abs/2410.17400
Automatic sound classification has a wide range of applications in machine listening, enabling context-aware sound processing and understanding. This paper explores methodologies for automatically classifying heterogeneous sounds characterized by hig
Externí odkaz:
http://arxiv.org/abs/2410.00980
In this work, we explore the use and reliability of Large Language Models (LLMs) in musicology. From a discussion with experts and students, we assess the current acceptance and concerns regarding this, nowadays ubiquitous, technology. We aim to go o
Externí odkaz:
http://arxiv.org/abs/2409.01864
Autor:
Ramoneda, Pedro, Eremenko, Vsevolod, D'Hooge, Alexandre, Parada-Cabaleiro, Emilia, Serra, Xavier
Estimating music piece difficulty is important for organizing educational music collections. This process could be partially automatized to facilitate the educator's role. Nevertheless, the decisions performed by prevalent deep-learning models are ha
Externí odkaz:
http://arxiv.org/abs/2408.00473
Recent advancements in music generation are raising multiple concerns about the implications of AI in creative music processes, current business models and impacts related to intellectual property management. A relevant discussion and related technic
Externí odkaz:
http://arxiv.org/abs/2407.14364
Automatically estimating the performance difficulty of a music piece represents a key process in music education to create tailored curricula according to the individual needs of the students. Given its relevance, the Music Information Retrieval (MIR
Externí odkaz:
http://arxiv.org/abs/2403.03947
Autor:
Alonso-Jiménez, Pablo, Pepino, Leonardo, Batlle-Roca, Roser, Zinemanas, Pablo, Bogdanov, Dmitry, Serra, Xavier, Rocamora, Martín
We present PECMAE, an interpretable model for music audio classification based on prototype learning. Our model is based on a previous method, APNet, which jointly learns an autoencoder and a prototypical network. Instead, we propose to decouple both
Externí odkaz:
http://arxiv.org/abs/2402.09318
Publikováno v:
The Version of Record of this contribution is published in MultiMedia Modeling. MMM 2024. Lecture Notes in Computer Science, vol 14565. Springer, Cham
Multi-modal deep learning techniques for matching free-form text with music have shown promising results in the field of Music Information Retrieval (MIR). Prior work is often based on large proprietary data while publicly available datasets are few
Externí odkaz:
http://arxiv.org/abs/2312.09207
Autor:
Narang, Jyoti, De La Vega, Viviana, Lizarraga, Xavier, Mayor, Oscar, Parra, Hector, Janer, Jordi, Serra, Xavier
Choral singing, a widely practiced form of ensemble singing, lacks comprehensive datasets in the realm of Music Information Retrieval (MIR) research, due to challenges arising from the requirement to curate multitrack recordings. To address this, we
Externí odkaz:
http://arxiv.org/abs/2311.08350