Music Signals Beat Tracking Based on Bidirectional Long Short Term Memory
In this research we developed a music signals beat tracking based on Bidirectional Long Short Term Memory (BLSTM) method. Predicting the beat of music is one of the Music Information Retrieval (MIR) research field. Humans can find the beats of the music automatically, like when we clap our hands or stomp our feet. Finding the beats of the music is a difficult task for machine or actuator. The music signal must be processed first to get the beats pattern as the time sequence. Various methods were proposed at the feature extraction, pattern recognition, and peak detection stages. BLSTM is applied to the magnitude spectrogram as the music feature which obtained from the feature extraction stage, and then to estimate the tempo we use autocorrelation as the peak detection stage. We used the ISMIR 2012 dataset as the training data and 10 songs of Ballroom and GTZAN dataset which has different musical character for each dataset as the testing data. The accuracy is proven by the calculation of f measure as a standard evaluation of beat tracking on music signals. The result shows that for the training data reach 86.3% f measure accuracy, Ballroom data 73.4% and GTZAN 65%
2019 International Electronics Symposium (IES)