In this paper we design a harmonic acoustic model for pitch detection. This model arranges conventional convolution and sparse convolution in a way such that the global harmonic patterns captured by sparse convolution are composed of the enough number of local patterns captured by layers of conventional convolution. When trained on the MAPS dataset, the harmonic model outperforms all existing pitch detection systems trained on the same dataset. Most impressively, when trained on MAPS with simple data augmentation, the harmonic model with an LSTM layer on top surpasses an up-to-date, more complex pitch detection system trained on the MAESTRO dataset to which complicated data augmentation is applied and whose training split is an order-of-magnitude larger than the training split of MAPS. The harmonic model has demonstrated potential to be used for advanced automatic music transcription (AMT) systems. In this paper we designed a harmonic acoustic model for pitch detection. This model effectively captures the complex frequency interactions characterizing polyphonic pitched music through conventional convolution and sparse convolution inspired by the harmonic structure of pitched music. In its pure form without RNN and data augmentation, the harmonic model outperformed most of the existing pitch detection systems. Most noticeably, when trained on MAPS and data augmentation is done, the harmonic model with an LSTM layer on top outdid the complex system in Hawthorne et al. (2019) trained on MAESTRO whose training split 15 times as large as the training split of MAPS. Thus, the harmonic model has shown great potential to be used for building advanced AMT systems. A possible future direction is to make more potential of complex spectrograms, instead of using only amplitude spectrograms. A mixture of signal can be inseparable in the real number domain but could be separable in the complex number domain. Trabelsi et al. (2018) has done some preliminary study in this direction. However, our own study showed that the technique of deep complex network proposed in Trabelsi et al. (2018) did not yield a performance comparable with that of real networks. Therefore, definitely more can be done.
harmonic acoustic model
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