Deepfake techniques mislead people’s cognition with high-quality fake videos and audios, and speech synthesis is an important tool to implement cognitive attacks, which mainly include Text-To-Speech (TTS) and Voice Conversion (VC). In this paper, we propose a method for audio spoof detection based on frequency band recalibration via sinc convolution and squeeze-excitation module, extracting features from the temporal waveform and emphasizing the frequency bands that are more useful on this task. Experimental results show that the proposed method outperforms other similar methods by 18.6% with an average EER of 7.23%, and achieve better generalizability on the detection of unseen spoofing methods, while the size of the model is reduced by 30.8%.