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Projected Belief Networks With Discriminative Alignment for Acoustic Event Classification: Rivaling State of the Art CNNs

2024-01-20 10:27:04
Paul M. Baggenstoss, Kevin Wilkinghoff, Felix Govaers, Frank Kurth

Abstract

The projected belief network (PBN) is a generative stochastic network with tractable likelihood function based on a feed-forward neural network (FFNN). The generative function operates by "backing up" through the FFNN. The PBN is two networks in one, a FFNN that operates in the forward direction, and a generative network that operates in the backward direction. Both networks co-exist based on the same parameter set, have their own cost functions, and can be separately or jointly trained. The PBN therefore has the potential to possess the best qualities of both discriminative and generative classifiers. To realize this potential, a separate PBN is trained on each class, maximizing the generative likelihood function for the given class, while minimizing the discriminative cost for the FFNN against "all other classes". This technique, called discriminative alignment (PBN-DA), aligns the contours of the likelihood function to the decision boundaries and attains vastly improved classification performance, rivaling that of state of the art discriminative networks. The method may be further improved using a hidden Markov model (HMM) as a component of the PBN, called PBN-DA-HMM. This paper provides a comprehensive treatment of PBN, PBN-DA, and PBN-DA-HMM. In addition, the results of two new classification experiments are provided. The first experiment uses air-acoustic events, and the second uses underwater acoustic data consisting of marine mammal calls. In both experiments, PBN-DA-HMM attains comparable or better performance as a state of the art CNN, and attain a factor of two error reduction when combined with the CNN.

Abstract (translated)

预计信念网络(PBN)是基于前馈神经网络(FFNN)的生成随机网络,具有可导的概率函数。生成函数通过“反向传播”操作进行。PBN有两个网络:一个在前进方向上操作的FFNN和一个在反向方向上操作的生成网络。两个网络基于相同的参数集存在,具有自己的成本函数,可以单独或共同训练。因此,PBN具有同时具备良好区分度和生成类别的品质。为了实现这一潜力,对于每个类别,单独对PBN进行训练,最大化给定类别的生成概率函数,同时最小化FFNN与“所有其他类别”之间的判别成本。这种技术被称为判别对齐(PBN-DA),将概率函数的轮廓与决策边界对齐,获得了与最先进的判别网络相当的分类性能,甚至超过了其性能。通过将隐马尔可夫模型(HMM)作为PBN的一个组成部分,称为PBN-DA-HMM,该技术可以进一步改进。本文对PBN、PBN-DA和PBN-DA-HMM进行了全面的讨论。此外,还提供了两个新的分类实验的结果。第一个实验使用空气声事件,第二个实验使用海洋动物叫声数据。在两个实验中,PBN-DA-HMM的性能与最先进的CNN相当,并且与CNN结合时,可以将误差降低一半。

URL

https://arxiv.org/abs/2401.11199

PDF

https://arxiv.org/pdf/2401.11199.pdf


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