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• W2714701844 abstract "This paper presents a new approach in understanding how deep neural networks (DNNs) work by applying homomorphic signal processing techniques. Focusing on the task of multi-pitch estimation (MPE), this paper demonstrates the equivalence relation between a generalized cepstrum and a DNN in terms of their structures and functionality. Such an equivalence relation, together with pitch perception theories and the recently established rectified-correlations-on-a-sphere (RECOS) filter analysis, provide an alternative way in explaining the role of the nonlinear activation function and the multi-layer structure, both of which exist in a cepstrum and a DNN. To validate the efficacy of this new approach, a new feature designed in the same fashion is proposed for pitch salience function. The new feature outperforms the one-layer spectrum in the MPE task and, as predicted, it addresses the issue of the missing fundamental effect and also achieves better robustness to noise." @default.
• W2714701844 created "2017-06-30" @default.
• W2714701844 creator A5038264558 @default.
• W2714701844 date "2017-06-26" @default.
• W2714701844 modified "2023-10-16" @default.
• W2714701844 title "Between Homomorphic Signal Processing and Deep Neural Networks: Constructing Deep Algorithms for Polyphonic Music Transcription" @default.
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• W2714701844 doi "https://doi.org/10.48550/arxiv.1706.08231" @default.
• W2714701844 hasPublicationYear "2017" @default.
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