Abstract
Communication with people is the most common phenomena of human. Mostly they can recognize the voice of their known one. Even the same thing is seen while recognizing a voice in the music. If the voice of the artist is known, then the recognition will be the easier one, but if the voice is not very familiar to the listener, it will be a tough job to identify the voice within music. Thus, singer recognition is one of the demanding areas of research by the implication of eligible algorithms in the domain of audio signal processing. There are different approaches that can be made for fulfilling the objective by attaining the goal of truncating the voice frequency range from the audio signal or it may be the detection of the peaks of the voice within that music. As music is polyphonic, so, the essential analysis is required to check for the frequency components and thereby detecting the peaks of the voice signal which can be an easier approach for such detection. In this paper, some songs are taken into consideration to create the training data and through which the neural network is trained. With that training data, a separate set of data is prepared which is used for testing. Apart from the application of the supervised learning procedure, with the implication of hyper parameter tuning, the efficiency is observed for the detection of the new and unknown signer to be detected. Essentially, the neural network works in this field fairly with about 99.29% accuracy and thus the detection is made with a satisfactory level.
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Biswas, S., Solanki, S.S. Speaker recognition: an enhanced approach to identify singer voice using neural network. Int J Speech Technol 24, 9–21 (2021). https://doi.org/10.1007/s10772-020-09698-8
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DOI: https://doi.org/10.1007/s10772-020-09698-8