## 3 Inter-Floor Noise Classification  
### Onset Detection  
In the previous studies [9; 10], a visually annotated inter-floor noise sample with a duration of 3 s was converted to a log-scaled Mel-spectrogram and classified into a source type and position category by a CNN-based classifier. However, the human visual annotation requires effort to annotate large amounts of data and knowledge.  
In the seismic signal-processing field, automatic seismic event-detection algorithms have been developed to replace laborious visual detection by humans. [PERSON] [41] developed an earthquake timing-detection algorithm using time averaging and zero-crossing rate measurement of signals over seismometers. [PERSON]'s algorithm was set as a baseline in many other onset-picking studies. A modified onset-picking method was developed in the acoustic emission field using the Akaike Information Criterion (AIC) [42], referring to [PERSON]'s algorithm and the extended work [43]. Applications of this approach in the acoustic emission field can be found [12; 44]. A broad-band maximum-likelihood method to estimate parameters and detect seismic events was studied [45]. Higher-order statistics (HOS)-based onset-picking methods were compared with [PERSON]'s algorithm and analyst's picks [46; 47]. These HOS-based methods were simple in implementation, showed similar results to analysts' picks, and demonstrated less detection failure under high noise levels than that of [PERSON]'s algorithm.  
Most of the signals in the inter-floor noise datasets are impact signals whose duration times are short and show a drastic energy rise at the event start positions. In this study, an onset-detection method using kurtosis is employed to detect the onset of the inter-floor noise signals. The kurtosis measures the heaviness of the tails representing the non-Gaussianity. This method was selected for the following reasons: (1) using a single HOS-property, kurtosis, is compact in implementation; (2) there exist very small differences between the onset detection results using skewness and kurtosis [46]; (3) the signals adopted in this study are obtained with a single sensor, so that exact onset-time picking is not required, unlike methods employing multiple channels of sensors to improve the performance of calculated properties, e.g., TDOA; and (4) CNN comprises shift-invariance in time axis in its input [48]. Equation (5) estimates the kurtosis of the \(M\)-sample sliding window, which returns a maximum value of \(\hat{\gamma}_{4}(k)\), \((k=M,M+1,\cdots,L)\) at an onset position for a given \(L\)-sample signal vector \(\mathbf{s}(m)\)[47].  
\[\hat{\gamma}_{4}(k) =(M-1)\frac{\sum_{m=k-M+1}^{k}(\mathbf{s}(m)-\hat{a}_{k})^{4}}{ \left[\sum_{m=k-M+1}^{k}(\mathbf{s}(m)-\hat{a}_{k})^{2}\right]^{2}}-3, \tag{5}\] \[\text{where }\hat{a}_{k}=\frac{1}{M}\sum_{m=k-M+1}^{k}\mathbf{s}(m).\]  
A confidence interval is usually considered to bound and ensure the probability of the signal's existence [47], which may be required in actual applications to reduce false alarms. However, this was not considered in this study, because the audio clips in the datasets do contain a single event such that a position with the maximum \(\hat{\gamma}_{4}(k)\) can be assumed as an onset. The window size \(M\) is usually selected to achieve the minimum difference between the analyst's pick and an estimated onset position. \(M=3000\) is sufficient to find an onset position.