LAUSR

The musical beat and style recognition have high application value in music information retrieval. However, the traditional methods mostly use a convolutional neural network (CNN) as the backbone and have poor performance. Accordingly, the present work chooses a recurrent neural network (RNN) in deep learning (DL) to identify musical beats and styles. The proposed model is applied to an interactive humanoid robot. First, DL-based musical beat and style recognition technologies are studied. On this basis, a note beat recognition method combining attention mechanism (AM) and independent RNN (IndRNN) [AM-IndRNN] is proposed. The AM-IndRNN can effectively avoid gradient vanishing and gradient exploding. Second, the audio music files are divided into multiple styles using the music signal's temporal features. A human dancing robot using a multimodal drive is constructed. Finally, the proposed method is tested. The results show that the proposed AM-IndRNN outperforms multiple parallel long short-term memory (LSTM) models and IndRNN in recognition accuracy (88.9%) and loss rate (0.0748). Therefore, the AM-optimized LSTM model has gained a higher recognition accuracy. The research results provide specific ideas for applying DL technology in musical beat and style recognition.