Improving Machine Hearing on Limited Data Sets
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F19%3APU133045" target="_blank" >RIV/00216305:26220/19:PU133045 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/8970740" target="_blank" >https://ieeexplore.ieee.org/document/8970740</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICUMT48472.2019.8970740" target="_blank" >10.1109/ICUMT48472.2019.8970740</a>
Alternative languages
Result language
angličtina
Original language name
Improving Machine Hearing on Limited Data Sets
Original language description
Convolutional neural network (CNN) architectures have originated and revolutionized machine learning for images. In order to take advantage of CNNs in predictive modeling with audio data, standard FFT-based signal processing methods are often applied to convert the raw audio waveforms into an image-like representations (e.g. spectrograms). Even though conventional images and spectrograms differ in their feature properties, this kind of pre-processing reduces the amount of training data necessary for successful training. In this contribution we investigate how input and target representations interplay with the amount of available training data in a music information retrieval setting. We compare the standard mel-spectrogram inputs with a newly proposed representation, called Mel scattering. Furthermore, we investigate the impact of additional target data representations by using and augmented target loss function which incorporates unused available information. We observe that all proposed methods outperform the standard mel-transform representation when using a limited data set and discuss their strengths and limitations. The source code for reproducibility of our experiments as well as intermediate results and model checkpoints are available in an online repository.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/LO1401" target="_blank" >LO1401: Interdisciplinary Research of Wireless Technologies</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2019 The 11th International Congress on Ultra Modern Telecommunications and Control Systems (ICUMT)
ISBN
978-1-7281-5764-1
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
1-6
Publisher name
IEEE
Place of publication
Dublin, Ireland
Event location
Dublin
Event date
Oct 28, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000540651700016