论文标题
将常规的音高检测算法与神经网络方法进行比较
Comparing Conventional Pitch Detection Algorithms with a Neural Network Approach
论文作者
论文摘要
尽管进行了很多研究，但传统的推销预测方法仍然并不完美。随着神经网络（NNS）的出现，研究人员希望创建一个胜过传统方法的基于NN的音高预测。本文比较了三种音高检测算法（PDA），PYIN，YAAPT和CREPE。 Pyin和Yaapt是考虑到时域和频域处理的常规方法。 Crepe利用受数据培训的深卷卷卷神经网络来估计音调。它涉及6个密集连接的卷积隐藏层，并确定给定输入信号的音高概率。将代表神经网络音调预测变量的可丽饼的性能与Pyin和Yaapt代表的更古典的方法进行了比较。功绩（FOM）的数字将包括未发音到发声的错误，发声到发声的错误，高音错误和细节错误的数量。
Despite much research, traditional methods to pitch prediction are still not perfect. With the emergence of neural networks (NNs), researchers hope to create a NN-based pitch predictor that outperforms traditional methods. Three pitch detection algorithms (PDAs), pYIN, YAAPT, and CREPE are compared in this paper. pYIN and YAAPT are conventional approaches considering time domain and frequency domain processing. CREPE utilizes a data-trained deep convolutional neural network to estimate pitch. It involves 6 densely connected convolutional hidden layers and determines pitch probabilities for a given input signal. The performance of CREPE representing neural network pitch predictors is compared to more classical approaches represented by pYIN and YAAPT. The figure of merit (FOM) will include the amount of unvoiced-to-voiced errors, voiced-to-voiced errors, gross pitch errors, and fine pitch errors.
