Neurons in the brain communicate information via punctual events called
spikes. The timing of spikes is thought to carry rich information, but it is
not clear how to leverage this in digital systems. We demonstrate that
event-based encoding is efficient for audio compression. To build this
event-based representation we use a deep binary auto-encoder, and under high
sparsity pressure, the model enters a regime where the binary event matrix is
stored more efficiently with sparse matrix storage algorithms. We test this on
the large MAESTRO dataset of piano recordings against vector quantized
auto-encoders. Not only does our "Spiking Music compression" algorithm achieve
a competitive compression/reconstruction trade-off, but selectivity and
synchrony between encoded events and piano key strikes emerge without
supervision in the sparse regime.

神经元通过称为脉冲的时机事件传递信息，在数字系统中如何利用这一信息仍不清楚。在本研究中，我们展示了事件编码在音频压缩中的有效性。我们使用深度二进制自编码器构建了这种基于事件的表示，在高稀疏度压力下，模型进入了二进制事件矩阵通过稀疏矩阵存储算法更有效存储的状态。我们将其与向量量化自编码器在大规模 MAESTRO 钢琴录音数据集上进行了测试。我们的 “脉冲音乐压缩” 算法不仅在压缩 / 重构平衡上取得了有竞争力的结果，而且在稀疏状态下不需要监督训练，编码事件和钢琴按键击中之间的选择性和同步性得以出现。

基于事件的自编码器的音频压缩技术

Spiking Music: Audio Compression with Event Based Auto-encoders

In this paper, we first present an explanation regarding the common
occurrence of spikes in the training loss when neural networks are trained with
stochastic gradient descent (SGD). We provide evidence that the spikes in the
training loss of SGD are "catapults", an optimization phenomenon originally
observed in GD with large learning rates in [Lewkowycz et al. 2020]. We
empirically show that these catapults occur in a low-dimensional subspace
spanned by the top eigenvectors of the tangent kernel, for both GD and SGD.
Second, we posit an explanation for how catapults lead to better generalization
by demonstrating that catapults promote feature learning by increasing
alignment with the Average Gradient Outer Product (AGOP) of the true predictor.
Furthermore, we demonstrate that a smaller batch size in SGD induces a larger
number of catapults, thereby improving AGOP alignment and test performance.

本文介绍了神经网络训练中出现巨大损失峰值现象的解释及其产生的优良泛化效果，它们通过在真实预测器的平均梯度外积上对特征学习的对齐来提高测试性能，并提供了一种方法来促进在 SGD 中找到更多的捕捉机制。