Robust singing detection in speech/music discriminator design
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02
Automatic Structure Detection for Popular Music
IEEE MultiMedia
Automatic summarization of music videos
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Towards efficient automated singer identification in large music databases
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Singing voice detection using perceptually-motivated features
Proceedings of the 15th international conference on Multimedia
Exploring Perceptual Based Timbre Feature for Singer Identification
Computer Music Modeling and Retrieval. Sense of Sounds
A novel framework for efficient automated singer identification in large music databases
ACM Transactions on Information Systems (TOIS)
On the improvement of singing voice separation for monaural recordings using the MIR-1K dataset
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Semantic region detection in acoustic music signals
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
Context-Aware features for singing voice detection in polyphonic music
AMR'11 Proceedings of the 9th international conference on Adaptive Multimedia Retrieval: large-scale multimedia retrieval and evaluation
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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.