FAF-Filt: Frequency-aware Fourier Filter for Sound Event Detection

Abstract

Capturing time-frequency patterns along the frequency axis, is crucial for the precision of sound event detection systems. Frequency dynamic convolution (FDY) and a series of its variants which incorporate frequency-adaptive kernels in standard 2D convolutions, have demonstrated remarkable performance, yet also suffered from high computational costs. To address the issue, we propose an efficient and light-weighted frequency-aware Fourier filter (FAF-Filt), which performs a 2D Fourier transform on features to the frequency domain and employs a learnable frequency-aware filter to process the transformed features, thereby integrating global information more effectively to extract decisive frequency components. In addition, frequency-adaptive convolution (FA-Conv) is adopted to further strengthen the representative ability of convolution, which incorporates the frequency-aware attention mechanism into the inputs and outputs of the convolutions. Experimental results exhibit superiority of the proposed method, achieving comparable performance with FDY-CRNN in terms of polyphonic sound event scores (PSDS) with a significantly 56% reduction in parameters.