# You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection
## 4 Results
### TUT Sound Event Detection  
Table 7 shows the results on the TUT sound event detection dataset. It also contains the results of the top three performers in the competition. For this competition, they adopted error rate [37] as the main metric. Note that a lower error rate indicates better performance of the algorithm. Furthermore, a segment size of 1 s was adopted to calculate segment-based metrics. The first place in the competition was the CRNN architecture [47]. They used 3 \(\times\) 3 kernels followed by B-GRU layers with 32 units. Their model was optimised by a random hyper-parameter search [48] for number of layers and units. The second place in the competition adopted a multi-input CNN with 3 \(\times\) 3 kernels and a bespoke feature extraction process. The third place adopted a B-GRU model. Note that all three models adopt segmentation-by-classification. YOHO obtained a better error rate than the CRNN [47], CNN [49], and B-GRU [50] models. To ensure that the improvement in performance was not attributed to data augmentation, we re-trained the best CRNN network [47] with SpecAugment. However, it worsened the performance of the algorithm. This may be because the CRNN uses segmentation-by-classification. Therefore, masking series of time steps leads to noise in the labels. However, the YOHO algorithm is relatively robust to this issue as it directly predicts boundaries through regression.  
Our results are not state-of-the-art on this dataset. [PERSON] et al. [51] adopted a Capsule Neural Network (CapsNet) and binaural short-time Fourier transform (STFT) for  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \(\mathbf{F_{overall}}\) & \(\mathbf{F_{music}}\) & \(\mathbf{F_{speech}}\) \\ \hline YOHO & **90.20** & 85.66 & **93.18** \\ CRNN [8] & 89.53 & **85.76** & 92.21 \\ CRNN [17] & 89.09 & 85.01 & 92.16 \\ CNN [45] & - & 54.78 & 90.9 \\ Logistic Regression [45] & - & 38.99 & 91.15 \\ ResNet [45] & - & 31.24 & 90.86 \\ MLP [46] & - & 49.36 & 77.18 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation on the MIREX music-speech detection dataset 2018. The results of other studies were obtained from the MIREX website [27]. The F-measures are presented as percentages. The values in bold indicate the largest number in each column.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \(\mathbf{F_{overall}}\) & \(\mathbf{F_{music}}\) & \(\mathbf{F_{speech}}\) \\ \hline YOHO & **97.22** & **98.20** & **94.89** \\ CRNN & 96.79 & 97.84 & 94.26 \\ CNN & 93.89 & 97.96 & 85.13 \\ \hline CRNN [17] & 96.37 & 97.37 & 94.00 \\ CRNN [8] & 96.24 & 97.30 & 93.80 \\ CNN [17] & 95.23 & 97.72 & 89.62 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results on our in-house test set for music-speech detection. The F-measures for overall, music, and speech are presented as percentages. The values in bold indicate the largest number in each column.  
feature extraction and obtained an error rate 0.58. [PERSON] et al. [52] presented a Capsule Neural Network Recurrent Neural Network (CapsNet-RNN) that obtained an error rate of 0.57. However, these optimisations were beyond the scope of this study. It is important to note that YOHO is a paradigm and not an architecture. We show that regression outperforms segmentation-by-classification for multiple models. Future research can explore how YOHO can be optimised by adopting a CapsNet-style architecture.