An investigation of CNN-LSTM music recognition algorithm in ethnic vocal technique singing

A HPSS separation algorithm considering time and frequency features is proposed to address the issue of poor performance in music style recognition and classification. A CNN network structure was designed and the influence of different parameters in the network structure on recognition rate was studied. A deep hash learning method is proposed to address the issues of weak feature expression ability and high feature dimension in existing CNN, which is combined with LSTM networks to integrate temporal dimension information. The results showed that compared to other models such as GRU+LSTM, the double-layer LSTM model used in the study had higher recognition results, with a size of over 75%. This indicates that combining feature learning with hash encoding learning can achieve higher accuracy. Therefore, this model is more suitable for music style recognition technology, which helps in music information retrieval and improves the classification accuracy of music recognition.