Singing voice recognition based on matching of spectrogram pattern

Singing voice recognition is a difficult topic in music information retrieval research area. The first approaches borrowed successful techniques widely used in automatic speech recognition (ASR) as speech and singing share similar acoustical feature since they are produced by the same apparatus. Moving from monophonic to polyphonic audio signal the problem become more complex as the background instrumental accompaniment is regarded as a noise source that has to be attenuated. This paper proposes a singing voice recognition algorithm that is able to automatically recognize the word in a singing signal with background music by using the concept of spectrogram pattern matching. The main idea is to apply both the spectrogram and the image processing methods to solve the problem of singing voice recognition. Each signal that accompanies music is analyzed and generated to its spectrogram that is used to train data for the classifier. Several classification functions are compared, such as Fisher classifier, feed-forward can effectively recognize the word in music with the accuracy rate more than 84%.