Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Dynamic Music Emotion Prediction is crucial to the emerging applications of music retrieval and recommendation. Considering the influence of temporal context and hierarchical structure on emotion in music,  propose a Deep Bidirectional Long Short-Term Memory (DBLSTM) based multi-scale regression method. In this method, a post-processing component is utilised for individual DBSLTM output to further enhance the ability of temporal context processing and a fusion component is to integrate the output of all DBLSTM models with different scales. In addition,  investigate how the difference of sequence length beten the training and predicting phase affects the performance of DBLSTM.  conduct our experiments on a public database of Emotion in Music task at MediaEval 2015, and the result shows that our method achieves significant improvement when compared with the state-of-art methods.
['Xinxing Li', 'Jiashen Tian', 'Mingxing Xu', 'Yishuang Ning', 'Lianhong Cai']
I am writing a research paper on a idea DBLSTM-based multi-scale fusion for dynamic emotion prediction in music
[{"from": "human", "value": "I am writing a research paper on a idea DBLSTM-based multi-scale fusion for dynamic emotion prediction in music"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Dynamic Music Emotion Prediction is crucial to the emerging applications of music retrieval and recommendation. Considering the influence of temporal context and hierarchical structure on emotion in music,  propose a Deep Bidirectional Long Short-Term Memory (DBLSTM) based multi-scale regression method. In this method, a post-processing component is utilised for individual DBSLTM output to further enhance the ability of temporal context processing and a fusion component is to integrate the output of all DBLSTM models with different scales. In addition,  investigate how the difference of sequence length beten the training and predicting phase affects the performance of DBLSTM.  conduct our experiments on a public database of Emotion in Music task at MediaEval 2015, and the result shows that our method achieves significant improvement when compared with the state-of-art methods."}]