The aim of the research work presented is an automatic singing voice quality/type recognition system. For this purpose a database containing singers’ sample recordings is constructed and parameters are extracted from recorded voices of trained and untrained singers of different voice types. Parameters, which are especially designed for the analysis of the singing voice, are analyzed and a feature vector is formed. Each of singers’ voice samples is judged by experts and information about voice type/quality is obtained. Parameters extracted are used in the training process of a neural network and the effectiveness of an automatic voice timbre/quality classification is tested by comparing automatic recognition results with subjective expert judgements. Finally, discussion of results is presented and conclusions are derived.
Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!
This paper costs $33 for non-members and is free for AES members and E-Library subscribers.