Classification and Separation of Audio and Music Signals

This chapter addresses the topic of classification and separation of audio and music signals. It is a very important and a challenging research area. The importance of classification process of a stream of sounds come up for the sake of building two different libraries: speech library and music library. However, the separation process is needed sometimes in a cocktail-party problem to separate speech from music and remove the undesired one. In this chapter, some existed algorithms for the classification process and the separation process are presented and discussed thoroughly. The classification algorithms will be divided into three categories. The first category includes most of the real time approaches. The second category includes most of the frequency domain approaches. However, the third category introduces some of the approaches in the time-frequency distribution. The approaches of time domain discussed in this chapter are the short-time energy (STE), the zero-crossing rate (ZCR), modified version of the ZCR and the STE with positive derivative, the neural networks, and the roll-off variance. The approaches of the frequency spectrum are specifically the roll-off of the spectrum, the spectral centroid and the variance of the spectral centroid, the spectral flux and the variance of the spectral flux, the cepstral residual, and the delta pitch. The time-frequency domain approaches have not been yet tested thoroughly in the process of classification and separation of audio and music signals. Therefore, the spectrogram and the evolutionary spectrum will be introduced and discussed. In addition, some algorithms for separation and segregation of music and audio signals, like the independent Component Analysis, the pitch cancelation and the artificial neural networks will be introduced.