Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Machine Hearing on Limited Data Sets   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 an 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.

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The categories of this paper are: q-bio
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The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
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The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
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The categories of this paper are: comp-gas
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The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
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The categories of this paper are: cond-mat
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The categories of this paper are: physics
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The categories of this paper are: math-ph
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The categories of this paper are: nlin