Figure 2. Stages of an Onset Detection Algorithm (Bello et al., 2005) Onsets are often categorised into two types. These are: 1) Hard onsets i.e., drums or percussion that exhibit sudden increases in energy 2) Soft onsets i.e., strings that exhibit a gradual change in energy over time. II.
LITERATURE REVIEW
Early detection functions were based on observing the signal in the time-domain and recording fluctuations in the amplitude envelope. This often-involved low pass filtering to reduce the effect of noise (Bello et al., 2005). Though important to acknowledge the methods based on time-domain signal features, these are not discussed further in this paper. Instead, spectral-based and deep learning-based methods are examined as these methods better reflect the trends of high-performing algorithms within the timeframe of interest (Böck, Arzt, Krebs, & Schedl, 2012; Eck, Douglas & Lacoste, 2007; Eyben et al., 2010; Roebel, 2005; Roebel, 2009). The majority of spectral onset detection algorithms discussed in this paper are based on the ShortTime Fourier transform (STFT). The STFT is used in order to analyse the signal’s spectral properties at specific time frames (Müller, 2016). A moving window function (often a Hamming window) multiplies the signal at various points in time and an FFT is performed on each of the windowed frames. This technique reveals which frequencies are present and at what times (fig 3).
