Aim: Heart murmur detection can provide a preliminary diagnosis of heart disease, and has become increasingly important in assisting clinical diagnosis and treatment in recent years. The purpose of this study is to construct an automatic detection system for heart murmur. Methods: We build a learnable filter-based transformer architecture. The learnable filter is embedded between the embedding layer and the encoder layer of the transformer. The parameters of the filter are optimized by Adam to adaptively represent any filter in the frequency domain, thereby achieving the effect of adaptive noise reduction. Then, the transformer encoder module captures the long-term dependencies of the heart sound signal, allowing the network to learn more effective features from the input signal. Finally, the final classification result will be obtained according to the voting rules we set. Results:Our(Bear_FH) method is trained and validated on a public dataset proposed by the challenge. In the formal phase of the challenge, the trained algorithm was tested using a hidden validation set, and we obtained challenge metric scores (weight accuracy and cost) of 0.367 and 19163, respectively.