Onset detection in polyphonic music

Abstract: In music analysis, the beginning of events in a music signal (i.e. sound onset detection) is important for such tasks as sound segmentation, beat recognition and automatic music transcription. The aim of the present work was to make an algorithm for sound onset detection with better performance than other state-of-the-art1 algorithms. Necessary theoretical background for spectral analysis on a sound signal is given with special focus on the Short-Time Fourier Transform (STFT) and the effects of applying a window to a signal. Previous works based on different approaches to sound onset detection were studied, and a possible improvement was observed for one such approach - namely the one developed by Bello, Duxbury, Davies, & Sandler (2004). The algorithm uses an STFT approach, analyzing a sound signal time frame by time frame. The algorithm’s detection is sequential in nature: It takes a frame from the STFT and makes an extrapolation to the next frame, assuming that the signal is constant. The difference between the extrapolated frame and the actual frame of the STFT constitutes the detection function. The proposed improvement lies in a combination of ideas from other algorithms, analyzing the signal with different frequency bands with frequency dependent settings and a modification of the extrapolation step. The proposed algorithm is compared to the original algorithm and an adaption by Dixon (2006) by analyzing 20 songs using three different window functions. The results were evaluated with the standards set by MIREX (2005-2016). The results of the proposed algorithm are encouraging, showing good recall, but fail to out-perform any of the algorithms it is compared to in both precision and the so-called F-measure. The shortcomings of the proposed algorithm leave room for further improvement, and a number of possible future modifications are exemplified.