Timbre and Melody Features for the Recognition of Vocal Activity and Instrumental Solos in Polyphonic Music

We propose the task of detecting instrumental solos in polyphonic music recordings, and the usage of a set of four audio features for vocal and instrumental activity detection. Three of the features are based on the prior extraction of the predominant melody line, and have not been used in the context of vocal/instrumental activity detection. Using a support vector machine hidden Markov model we conduct 14 experiments to validate several combinations of our proposed features. Our results clearly demonstrate the benefit of combining the features: the best performance was always achieved by combining all four features. The top accuracy for vocal activity detection is 87.2%. The more difficult task of detecting instrumental solos equally benefits from the combination of all features and achieves an accuracy of 89.8% and a satisfactory precision of 61.1%. With this paper we also release to the public the 102 annotations we used for training and testing. The annotations offer not only vocal/nonvocal labels, but also distinguish between female and male singers, and different solo instruments.