自适应埃尔米特三次样条小波学习在生物声学啁啾中的应用

Randall Balestriero, H. Glotin
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引用次数: 2

摘要

声学监测用于研究海洋中的海洋哺乳动物。由于数据量大,对捕获的声音进行自动分析几乎是必不可少的。深度学习方法是一种有效的方法,但声学特征往往不适应。卷积神经网络可以看作是一种最优的核分解,但是它需要大量的训练数据来学习它的核。另一种方法是使用预施加的核,因此不需要任何数据量,这是散射框架,它施加核小波滤波器。我们的研究重点是基于三次样条学习表示的生物声信号的自适应时频分解。给出了该模型的理论推导,并对包括蓝鲸鸣叫声在内的各种信号进行了有效的实际应用。
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Wavelet Learning by Adaptive Hermite Cubic Splines applied to Bioacoustic Chirps
Acoustic monitoring is used to study marine mammals in oceans. Automated analysis for captured sound is almost essential because of the large quantity of data. The deep learning approach is an efficient method, however acoustic features are often not adapted. Convolutional Neural Net can be seen as an optimal kernel decomposition, nevertheless it requires large amount of training data to learn its kernels. An alternative using pre-imposed kernels and thus not requiring any amount of data is the scattering framework which imposes as kernels wavelet filters. Our research focuses on adaptive time-frequency decomposition of bioacoustic signal, based on cubic spline learning representation. We give the theoretical derivations of the model, and demonstrates efficient real applications of various signal, including chirps of songs of Blue Whale.
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