State-of-the-art strided audio encoders impose predictable alias-collapse and resolution bottlenecks on frequency primitives; Gabor Latent Refactorization recovers much of the lost separability post-hoc.
Raw Waveform-based Audio Classification Using Sample-level CNN Architectures
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abstract
Music, speech, and acoustic scene sound are often handled separately in the audio domain because of their different signal characteristics. However, as the image domain grows rapidly by versatile image classification models, it is necessary to study extensible classification models in the audio domain as well. In this study, we approach this problem using two types of sample-level deep convolutional neural networks that take raw waveforms as input and uses filters with small granularity. One is a basic model that consists of convolution and pooling layers. The other is an improved model that additionally has residual connections, squeeze-and-excitation modules and multi-level concatenation. We show that the sample-level models reach state-of-the-art performance levels for the three different categories of sound. Also, we visualize the filters along layers and compare the characteristics of learned filters.
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Structural Bottlenecks on Frequency Representation in End-to-End Audio Models
State-of-the-art strided audio encoders impose predictable alias-collapse and resolution bottlenecks on frequency primitives; Gabor Latent Refactorization recovers much of the lost separability post-hoc.