Results 111 to 120 of about 4,447 (146)

PolyRNN: A time-resolved model of polyphonic musical expectations aligned with human brain responses

Robert P, Van Cang MP, Mercier M, Trébuchon A, Bartolomei F, Arnal LH, Morillon B, Doelling K.   +7 more
europepmc   +1 more source

A music source separation method integrating time-frequency decoupling and mamba-based state space modeling. [PDF]

Sci Rep
Zhang C, Zheng J, Cao M.
europepmc   +1 more source

Detection and classification of acoustic scenes and events: an IEEE AASP challenge [PDF]

Benetos, E., Giannoulis, D., Lagrange, M., Plumbley, M. D., Rossignol, M., Stowell, D.   +5 more
core  

Cortical activity during naturalistic music listening reflects short-range predictions based on long-term experience

, 2022
Kern P, Heilbron M, de Lange FP, Spaak E.   +3 more
europepmc   +1 more source

Development of Auditory and Spontaneous Movement Responses to Music over the First Postnatal Year

Nguyen T, Bigand F, Reisner S, Koul A, Bianco R, Markova G, Hoehl S, Novembre G.   +7 more
europepmc   +1 more source

Syncopation as structure bootstrapping: the role of asymmetry in rhythm and language. [PDF]

Front Psychol
Fiorin G, Delfitto D.
europepmc   +1 more source

Hydrogen bonding heterogeneity correlates with protein folding transition state passage time as revealed by data sonification. [PDF]

Proc Natl Acad Sci U S A
Scaletti C, Russell PPS, Hebel KJ, Rickard MM, Boob M, Danksagmüller F, Taylor SA, Pogorelov TV, Gruebele M.   +8 more
europepmc   +1 more source

Stochastic properties of musical time series. [PDF]

Nat Commun
Nelias C, Geisel T.
europepmc   +1 more source

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Duration-Controlled LSTM for Polyphonic Sound Event Detection

IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2017
This paper presents a new hybrid approach called duration-controlled long short-term memory (LSTM) for polyphonic sound event detection (SED). It builds upon a state-of-the-art SED method that performs frame-by-frame detection using a bidirectional LSTM recurrent neural network (BLSTM), and incorporates a duration-controlled modeling technique based on
Tomoki Hayashi, Shinji Watanabe, Tomoki Toda, Takaaki Hori, Jonathan Le Roux, Kazuya Takeda   +5 more
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Fully Convolutional DenseNet based polyphonic sound event detection

2018 International Conference on Cloud Computing, Big Data and Blockchain (ICCBB), 2018
As a technology of context analysis, the detective method of polyphonic sound event detection has a widespread prospect of application. In this paper, a detective method of polyphonic sound event were proposed to resolve the challenge in IEEE DCASE2017 task 3 based on full convolutional DenseNet.
He Zhe, Li Ying
openaire   +3 more sources