TY - GEN
T1 - A comparison of audio signal preprocessing methods for deep neural networks on music tagging
AU - Choi, Keunwoo
AU - Fazekas, György
AU - Sandler, Mark
AU - Cho, Kyunghyun
N1 - Funding Information:
∗FAST IMPACt EPSRC Grant EP/L019981/1 and the European Commission H2020 research and innovation grant AudioCommons (688382). Mark Sandler acknowledges the support of the Royal Society as a recipient of a Wolfson Research Merit Award.
Funding Information:
†Kyunghyun Cho thanks the support by Facebook, Google (Google Faculty Award 2016) and NVidia (GPU Center of Excellence 2015-2016) Fig. 1: Network structure of the 5-layer ConvNet. N refers to the number of feature maps (which is set to 32 for all layers in this paper) while W refers to the weights matrix of the fully-connected output layer.) Table 1: Details of the ConvNet architecture shown in Figure 1. 2-dimensional convolutional layer is specified by (channel, (kernel lengths in frequency, time)). Pooling layer is specified by (pooling length in frequency and time)
PY - 2018/11/29
Y1 - 2018/11/29
N2 - In this paper, we empirically investigate the effect of audio preprocessing on music tagging with deep neural networks. We perform comprehensive experiments involving audio preprocessing using different time-frequency representations, logarithmic magnitude compression, frequency weighting, and scaling. We show that many commonly used input preprocessing techniques are redundant except magnitude compression.
AB - In this paper, we empirically investigate the effect of audio preprocessing on music tagging with deep neural networks. We perform comprehensive experiments involving audio preprocessing using different time-frequency representations, logarithmic magnitude compression, frequency weighting, and scaling. We show that many commonly used input preprocessing techniques are redundant except magnitude compression.
UR - http://www.scopus.com/inward/record.url?scp=85059824782&partnerID=8YFLogxK
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U2 - 10.23919/EUSIPCO.2018.8553106
DO - 10.23919/EUSIPCO.2018.8553106
M3 - Conference contribution
AN - SCOPUS:85059824782
T3 - European Signal Processing Conference
SP - 1870
EP - 1874
BT - 2018 26th European Signal Processing Conference, EUSIPCO 2018
PB - European Signal Processing Conference, EUSIPCO
T2 - 26th European Signal Processing Conference, EUSIPCO 2018
Y2 - 3 September 2018 through 7 September 2018
ER -