Automatic singer identification

The singer’s information is essential in organizing, browsing and retrieving music collections. In this paper, a system for automatic singer identification is developed which recognizes the singer of a song by analyzing the music signal. Meanwhile, songs which are similar in terms of singer’s voice are clustered. The proposed scheme follows the framework of common speaker identification systems, but special efforts are made to distinguish the singing voice from instrumental sounds in a song. A statistical model is trained for each singer’s voice with typical song(s) of the singer. Then, for a song to be identified, the starting point of singing voice is detected and a portion of the song is excerpted from that point. Audio features are extracted and matched with singers’ voice models in the database. The song is assigned to the model having the best match. Promising results are obtained on a small set of samples, and accuracy rates of around 80% are achieved.