Are deep neural networks really learning relevant features?

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Abstract

In recent years deep neural networks (DNNs) have become a popular choice for audio content analysis. This may be attributed to various factors including advancements in training algorithms, computational power, and the potential for DNNs to implicitly learn a set of feature detectors. We have recently re-examined two works \cite{sigtiaimproved}\cite{hamel2010learning} that consider DNNs for the task of music genre recognition (MGR). These papers conclude that frame-level features learned by DNNs offer an improvement over traditional, hand-crafted features such as Mel-frequency cepstrum coefficients (MFCCs). However, these conclusions were drawn based on training/testing using the GTZAN dataset, which is now known to contain several flaws including replicated observations and artists \cite{sturm2012analysis}. We illustrate how considering these flaws dramatically changes the results, which leads one to question the degree to which the learned frame-level features are actually useful for MGR. We make available a reproducible software package allowing other researchers to completely duplicate our figures and results.
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In recent years deep neural networks (DNNs) have become a popular choice for audio content analysis. This may be attributed to various factors including advancements in training algorithms, computational power, and the potential for DNNs to implicitly learn a set of feature detectors. We have recently re-examined two works \cite{sigtiaimproved}\cite{hamel2010learning} that consider DNNs for the task of music genre recognition (MGR). These papers conclude that frame-level features learned by DNNs offer an improvement over traditional, hand-crafted features such as Mel-frequency cepstrum coefficients (MFCCs). However, these conclusions were drawn based on training/testing using the GTZAN dataset, which is now known to contain several flaws including replicated observations and artists \cite{sturm2012analysis}. We illustrate how considering these flaws dramatically changes the results, which leads one to question the degree to which the learned frame-level features are actually useful for MGR. We make available a reproducible software package allowing other researchers to completely duplicate our figures and results.
Original languageEnglish
Publication date2015
StatePublished - 2015
Publication categoryResearch
Peer-reviewedYes
EventDigital Music Research Network 9 - Queen Mary University of London, London, United Kingdom
Duration: 16 Dec 201416 Dec 2014

Workshop

WorkshopDigital Music Research Network 9
LocationQueen Mary University of London
CountryUnited Kingdom
CityLondon
Period16/12/201416/12/2014

Projects

ID: 206535669