HarmoF0: Logarithmic Scale Dilated Convolution For Pitch Estimation

• URL: http://arxiv.org/abs/2205.01019v1
• Date: Mon, 2 May 2022 16:45:20 GMT
• Title: HarmoF0: Logarithmic Scale Dilated Convolution For Pitch Estimation
• Authors: Weixing Wei, Peilin Li, Yi Yu, Wei Li
• Abstract summary: This paper introduces a multiple rates dilated causal convolution (MRDC-Conv) method to capture the harmonic structure in logarithmic scale spectrograms efficiently. We propose HarmoF0, a fully convolutional network, to evaluate the MRDC-Conv and other dilated convolutions in pitch estimation. The results show that this model outperforms the DeepF0, yields state-of-the-art performance in three datasets, and simultaneously reduces more than 90% parameters.
• Score: 7.5089093564620155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sounds, especially music, contain various harmonic components scattered in the frequency dimension. It is difficult for normal convolutional neural networks to observe these overtones. This paper introduces a multiple rates dilated causal convolution (MRDC-Conv) method to capture the harmonic structure in logarithmic scale spectrograms efficiently. The harmonic is helpful for pitch estimation, which is important for many sound processing applications. We propose HarmoF0, a fully convolutional network, to evaluate the MRDC-Conv and other dilated convolutions in pitch estimation. The results show that this model outperforms the DeepF0, yields state-of-the-art performance in three datasets, and simultaneously reduces more than 90% parameters. We also find that it has stronger noise resistance and fewer octave errors.

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