Zobrazeno 1 - 10
of 169
pro vyhledávání: '"BIMBOT, FRÉDÉRIC"'
Publikováno v:
Eurospeech 1995, Sep 1995, Madrid, Spain. pp.337-340
Second-order statistical methods show very good results for automatic speaker identification in controlled recording conditions. These approaches are generally used on the entire speech material available. In this paper, we study the influence of the
Externí odkaz:
http://arxiv.org/abs/2402.16429
Structural segmentation of music refers to the task of finding a symbolic representation of the organisation of a song, reducing the musical flow to a partition of non-overlapping segments. Under this definition, the musical structure may not be uniq
Externí odkaz:
http://arxiv.org/abs/2212.11054
Music Structure Analysis (MSA) consists of representing a song in sections (such as ``chorus'', ``verse'', ``solo'' etc), and can be seen as the retrieval of a simplified organization of the song. This work presents a new algorithm, called Convolutiv
Externí odkaz:
http://arxiv.org/abs/2210.15356
Music Structure Analysis (MSA) consists in segmenting a music piece in several distinct sections. We approach MSA within a compression framework, under the hypothesis that the structure is more easily revealed by a simplified representation of the or
Externí odkaz:
http://arxiv.org/abs/2202.04981
Nonnegative Tucker decomposition (NTD), a tensor decomposition model, has received increased interest in the recent years because of its ability to blindly extract meaningful patterns, in particular in Music Information Retrieval. Nevertheless, exist
Externí odkaz:
http://arxiv.org/abs/2110.14434
The ability of deep neural networks to learn complex data relations and representations is established nowadays, but it generally relies on large sets of training data. This work explores a "piece-specific" autoencoding scheme, in which a low-dimensi
Externí odkaz:
http://arxiv.org/abs/2110.14437
Uncovering audio patterns in music with Nonnegative Tucker Decomposition for structural segmentation
Publikováno v:
21st International Society for Music Information Retrieval Conference (ISMIR), Montr\'eal, Canada, 2020, 788-794
Recent work has proposed the use of tensor decomposition to model repetitions and to separate tracks in loop-based electronic music. The present work investigates further on the ability of Nonnegative Tucker Decompositon (NTD) to uncover musical patt
Externí odkaz:
http://arxiv.org/abs/2104.08580
Autor:
Amato, Giuseppe, Behrmann, Malte, Bimbot, Frédéric, Caramiaux, Baptiste, Falchi, Fabrizio, Garcia, Ander, Geurts, Joost, Gibert, Jaume, Gravier, Guillaume, Holken, Hadmut, Koenitz, Hartmut, Lefebvre, Sylvain, Liutkus, Antoine, Lotte, Fabien, Perkis, Andrew, Redondo, Rafael, Turrin, Enrico, Vieville, Thierry, Vincent, Emmanuel
Thanks to the Big Data revolution and increasing computing capacities, Artificial Intelligence (AI) has made an impressive revival over the past few years and is now omnipresent in both research and industry. The creative sectors have always been ear
Externí odkaz:
http://arxiv.org/abs/1905.04175
Autor:
Bertin, Nancy, Camberlein, Ewen, Lebarbenchon, Romain, Vincent, Emmanuel, Sivasankaran, Sunit, Illina, Irina, Bimbot, Frédéric
Publikováno v:
In Speech Communication January 2019 106:68-78
Convolutive source separation is often done in two stages: 1) estimation of the mixing filters and 2) estimation of the sources. Traditional approaches suffer from the ambiguities of arbitrary permutations and scaling in each frequency bin of the est
Externí odkaz:
http://arxiv.org/abs/1111.2848