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Music structure analysis based on an LSTM-HSMM hybrid model
2020
Zenodo
Preserved Fulltext
The experimental results show that the proposed LSTM-HSMM hybrid model outperformed a conventional HSMM. ...
This paper describes a statistical music structure analysis method that splits an audio signal of popular music into musically meaningful sections at the beat level and classifies them into predefined ...
Yoshii, "Music structure analysis based on an LSTM-HSMM hybrid model", in Proc. of the 21st Int. Society for Music Information Retrieval Conf., Montréal, Canada, 2020. ...
doi:10.5281/zenodo.4245362
fatcat:4mutrgwjhjdxdmxccbsu45vspu
Machine Learning for Data-Driven Movement Generation: a Review of the State of the Art
[article]
2019
arXiv
pre-print
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We cover topics such as high-level movement characterization, training data, features representation, machine learning models, and evaluation methods. ...
Movement Analysis research, and dance and music studies. ...
Crnkovic-Friis and Crnkovic-Friis (2016) 29 train an Long Short-Term Memory (LSTM) on 3D joint positions of a dancer. ...
arXiv:1903.08356v1
fatcat:wtqawbramvdx3kz6ffgp2sv3ja
Supervised Metric Learning for Music Structure Features
[article]
2022
arXiv
pre-print
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Music structure analysis (MSA) methods traditionally search for musically meaningful patterns in audio: homogeneity, repetition, novelty, and segment-length regularity. ...
The trained model extracts features that can be used in existing MSA algorithms. ...
Taking the converse approach, [2] used supervision to model how traditional features (MFCCs, CQT, etc.) relate to music structure, using an LSTM combined with a Hidden semi-Markov Model. ...
arXiv:2110.09000v2
fatcat:jkckpxf4tjf3lhgcvss4ukcktq
Supervised Metric Learning For Music Structure Features
2021
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Music structure analysis (MSA) methods traditionally search for musically meaningful patterns in audio: homogeneity, repetition, novelty, and segment-length regularity. ...
The trained model extracts features that can be used by existing MSA algorithms. ...
Taking the converse approach, [2] used supervision to model how traditional features (MFCCs, CQT, etc.) relate to music structure, using an LSTM combined with a Hidden semi-Markov Model. ...
doi:10.5281/zenodo.5624427
fatcat:cqqt5mmqzbefvcl55nbfrw245q
A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning
2021
Sensors
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The hybrid structure that combines a CNN 1D and a LSTM is the best performing one.
Outlines A number of algorithms have been studied for HAR in smart homes. ...
[74] studied different neural network architectures, such as MLP, CNN, LSTM, GRU, or hybrid structures, to evaluate which structure is the most efficient. ...
doi:10.3390/s21186037
pmid:34577243
fatcat:vo5aqjlnbvhbtiktsamoybh2i4
Lombard Speech Synthesis Using Transfer Learning in a Tacotron Text-to-Speech System
2019
Interspeech 2019
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The performance of the vocoders was evaluated in the context of analysis-synthesis and text-to-speech (TTS). Here, the TTS systems were developed using an LSTM-based acoustic model. ...
LSTM-based SPSS system that was trained on normal speaking style. ...
doi:10.21437/interspeech.2019-1333
dblp:conf/interspeech/BollepalliJA19
fatcat:5uz43svog5erzev5nzakdnc4qe
Intonation Modelling for Speech Synthesis and Emphasis Preservation
2017
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A motivation for such a model is its theoretical language independence, based on the fact that humans share the same vocal apparatus. ...
An automatic parameter extraction method which integrates a perceptually relevant measure is proposed with the model. This approach is evaluated and compared with the standard command-response model. ...
as only an analysis and resynthesis is performed. ...
doi:10.5075/epfl-thesis-7520
fatcat:4g5qshl5mrgfbacc54yjeeqzbm