Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The separation of the lead vocals from the background accompaniment in audio recordings is a challenging task. Recently, an efficient method called REPET (REpeating Pattern Extraction Technique) has been proposed to extract the repeating background from the non-repeating foreground. While effective on individual sections of a song, REPET does not allow for variations in the background (e.g. verse vs. chorus), and is thus limited to short excerpts only.  overcome this limitation and generalize REPET to permit the processing of complete musical tracks. The proposed algorithm tracks the period of the repeating structure and computes local estimates of the background pattern. Separation is performed by soft time-frequency masking, based on the deviation beten the current observation and the estimated background pattern. Evaluation on a dataset of 14 complete tracks shows that this method can perform at least as ll as a recent competitive music/voice separation method, while being computationally efficient.
['Antoine Liutkus', 'Zafar Rafii', 'Roland Badeau', 'Bryan Pardo', 'Gaël Richard']
I am writing a research paper on a idea Adaptive filtering for music/voice separation exploiting the repeating musical structure
[{"from": "human", "value": "I am writing a research paper on a idea Adaptive filtering for music/voice separation exploiting the repeating musical structure"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The separation of the lead vocals from the background accompaniment in audio recordings is a challenging task. Recently, an efficient method called REPET (REpeating Pattern Extraction Technique) has been proposed to extract the repeating background from the non-repeating foreground. While effective on individual sections of a song, REPET does not allow for variations in the background (e.g. verse vs. chorus), and is thus limited to short excerpts only.  overcome this limitation and generalize REPET to permit the processing of complete musical tracks. The proposed algorithm tracks the period of the repeating structure and computes local estimates of the background pattern. Separation is performed by soft time-frequency masking, based on the deviation beten the current observation and the estimated background pattern. Evaluation on a dataset of 14 complete tracks shows that this method can perform at least as ll as a recent competitive music/voice separation method, while being computationally efficient."}]