Combining monaural source separation with Long Short-Term Memory for increased robustness in vocalist gender recognition

We present a novel and unique combination of algorithms to detect the gender of the leading vocalist in recorded popular music. Building on our previous successful approach that enhanced the harmonic parts by means of Non-Negative Matrix Factorization (NMF) for increased accuracy, we integrate on the one hand a new source separation algorithm specifically tailored to extracting the leading voice from monaural recordings. On the other hand, we introduce Bidirectional Long Short-Term Memory Recurrent Neural Networks (BLSTM-RNNs) as context-sensitive classifiers for this scenario, which have lately led to great success in Music Information Retrieval tasks. Through a combination of leading voice separation and BLSTM networks, as opposed to a baseline approach using Hidden Naive Bayes on the original recordings, the accuracy of simultaneous detection of vocal presence and vocalist gender on beat level is improved by up to 10% absolute. Furthermore, using this technique we achieve 91.6% accuracy in determining the gender of the predominant vocalist on song level, which is 4% absolute above our previous best result.