Automatic language identification for singing is a topic that has not received much attention in the past years. Possible application scenarios include searching for musical pieces in a certain language, improvement of similarity search algorithms for music, and improvement of regional music classification and genre classification. It could also serve to mitigate the "glass ceiling" effect. Most existing approaches employ PPRLM processing (Parallel Phone Recognition followed by Language Modeling). We present a new approach for singing language identification. PLP, MFCC, and SDC features are extracted from audio files and then passed through an i-vector extractor. This algorithm reduces the training data for each sample to a single 450-dimensional feature vector. We then train Neural Networks and Support Vector Machines on these feature vectors. Due to the reduced data, the training process is very fast. The results are comparable to the state of the art, reaching accuracies of 83% on a large speech corpus and 78% on acapella singing. In contrast to PPRLM approaches, our algorithm does not require phoneme-wise annotations and is easier to implement.

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