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pro vyhledávání: '"Müller Meinard"'
In this article, we investigate the notion of model-based deep learning in the realm of music information research (MIR). Loosely speaking, we refer to the term model-based deep learning for approaches that combine traditional knowledge-based methods
Externí odkaz:
http://arxiv.org/abs/2406.11540
Autor:
Tur Bogac, Echternach Matthias, Turowski Stefan, Müller Meinard, Köberlein Marie, Döllinger Michael, Kniesburges Stefan
Publikováno v:
Acta Acustica, Vol 6, p 50 (2022)
Wearing face coverings became one essential tool in order to prohibit virus transmission during the COVID-19 pandemic. In comparison to speaking and breathing, singing emits a much higher amount of aerosol particles. Therefore, there are situations i
Externí odkaz:
https://doaj.org/article/74bf77b5b8754b4696f8bbbe5c2d7e78
Generating multi-instrument music from symbolic music representations is an important task in Music Information Retrieval (MIR). A central but still largely unsolved problem in this context is musically and acoustically informed control in the genera
Externí odkaz:
http://arxiv.org/abs/2309.12283
To model the periodicity of beats, state-of-the-art beat tracking systems use "post-processing trackers" (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo. For ex
Externí odkaz:
http://arxiv.org/abs/2308.10355
Soft dynamic time warping (SDTW) is a differentiable loss function that allows for training neural networks from weakly aligned data. Typically, SDTW is used to iteratively compute and refine soft alignments that compensate for temporal deviations be
Externí odkaz:
http://arxiv.org/abs/2308.05429
Many tasks in music information retrieval (MIR) involve weakly aligned data, where exact temporal correspondences are unknown. The connectionist temporal classification (CTC) loss is a standard technique to learn feature representations based on weak
Externí odkaz:
http://arxiv.org/abs/2304.05032
For expressive music, the tempo may change over time, posing challenges to tracking the beats by an automatic model. The model may first tap to the correct tempo, but then may fail to adapt to a tempo change, or switch between several incorrect but p
Externí odkaz:
http://arxiv.org/abs/2210.06817
Attention-based Transformer models have been increasingly employed for automatic music generation. To condition the generation process of such a model with a user-specified sequence, a popular approach is to take that conditioning sequence as a primi
Externí odkaz:
http://arxiv.org/abs/2111.04093
Autor:
Abeßer, Jakob, Müller, Meinard
The deployment of machine listening algorithms in real-life applications is often impeded by a domain shift caused for instance by different microphone characteristics. In this paper, we propose a novel domain adaptation strategy based on disentangle
Externí odkaz:
http://arxiv.org/abs/2110.13586
Unsupervised Domain Adaptation for Acoustic Scene Classification Using Band-Wise Statistics Matching
The performance of machine learning algorithms is known to be negatively affected by possible mismatches between training (source) and test (target) data distributions. In fact, this problem emerges whenever an acoustic scene classification system wh
Externí odkaz:
http://arxiv.org/abs/2005.00145