We propose a learnable content adaptive front end for audio signal processing. Before the modern advent of deep learning, we used fixed representation non-learnable front-ends like spectrogram or mel-spectrogram with/without neural architectures. With convolutional architectures supporting various applications such as ASR and acoustic scene understanding, a shift to a learnable front ends occurred in which both the type of basis functions and the weight were learned from scratch and optimized for the particular task of interest. With the shift to transformer-based architectures with no convolutional blocks present, a linear layer projects small waveform patches onto a small latent dimension before feeding them to a transformer architecture. In this work, we propose a way of computing a content-adaptive learnable time-frequency representation. We pass each audio signal through a bank of convolutional filters, each giving a fixed-dimensional vector. It is akin to learning a bank of finite impulse-response filterbanks and passing the input signal through the optimum filter bank depending on the content of the input signal. A content-adaptive learnable time-frequency representation may be more broadly applicable, beyond the experiments in this paper.

翻译：我们提出一种可学习的内容自适应前端，用于音频信号处理。在深度学习现代兴起之前，我们使用固定的、不可学习的表示前端，如频谱图或梅尔频谱图，并搭配或不搭配神经架构。随着卷积架构支持语音识别和声学场景理解等多种应用，出现了向可学习前端的转变，其中基函数类型和权重均从零开始学习，并针对特定任务进行优化。随着向无卷积块的基于Transformer架构的转变，在将小波形片段输入Transformer架构之前，线性层将其投影到较小的潜在维度上。在这项工作中，我们提出了一种计算内容自适应可学习时频表示的方法。我们将每个音频信号通过一组卷积滤波器，每个滤波器给出一个固定维度的向量。这类似于学习一组有限脉冲响应滤波器组，并根据输入信号的内容将输入信号通过最优滤波器组。内容自适应可学习时频表示可能具有更广泛的适用性，超越本文中的实验。