Abstract
Singer identification is a difficult topic in music information Retriveal research area. Because the background instrumental accompaniment in audio music is regarded as noise source that has to reduce a performance.
This paper proposes a singer identification algorithm thai is able to automatically identify a singer in an audio music signal with background music by using Time-Frequency audio feature. The main idea is used a spectrogram to able effective Time-Frequency feature and used as the input for classification. The proposed technique is test with 20 different singer. Sereval classification technique are compared,such as Feed-Forward Neural Network, k-Nearest Neighbor (kNN) and Minimum least square linear classifier(Fisher). The experimental result on singer identification using a spectrogram with Feed-Forward Neural Networkand and k-Nearest Neighbor (kNN) can effectively identify the singer in music signal with background music more than 92%.
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Doungpaisan, P. (2011). Singer Identification Using Time-Frequency Audio Feature. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21090-7_57
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DOI: https://doi.org/10.1007/978-3-642-21090-7_57
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