# You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection
## 4 Results
### Speed of Prediction  
In this section, we compare the inference times of YOHO, CNN and CRNN models for music-speech detection. This experiment was performed on the in-house test set explained in Section 3.1. To calculate the inference time, the prediction was made over the entire test set. Later, the inference time was divided by the number of hours of audio to obtain the average time taken per hour of audio. As we are adopting Google Colab for experiments, we ensured that all models were tested within the same runtime session. This way, we ensure that the same computing resources were given to YOHO, CNN, and CRNN. While training the models in earlier runtime sessions, we had stored their weights on Google Drive. When running the experiment to calculate inference times, these weights were loaded from Google Drive. Note that separate runtime sessions were used to calculate inference times over CPU and GPU as shown in Figure 4, however the same session was used for inter-model comparison. Some important aspects of the system configuration  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Error Rate** \\ \hline CapsNet-RNN [52] & **0.57** \\ CapsNet [51] & 0.59 \\ YOHO & 0.75 \\ CRNN [47] & 0.79 \\ CNN [49] & 0.81 \\ B-GRU [50] & 0.83 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results on the TUT sound event detection dataset. The value in bold indicates the algorithm with the lowest error rate.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **F\({}_{\text{overall}}\)** \\ \hline CRNN with envelope estimation [53] & **64.70** \\ YOHO & 59.50 \\ CNN [9] & 56.88 \\ CRNN [9] & 55.96 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Segment-based overall F-measure on the Urban-SED dataset. The value in bold indicates the algorithm with the highest F-measure.  
were--Intel Xeon CPU processor, 12 GB RAM, and Tesla P100 GPU (only when GPU was used).  
Figure 4 compares the inference times of YOHO, CNN and CRNN models for music-speech detection on the in-house test set. The CNN and CRNN models were explained in Section 2.7. YOHO and the CNN had exactly the same number of parameters, which is 3.9 million. The only difference is that the CNN adopts frame-based classification instead of regression. The CRNN model had 1.3 million parameters, which was less than the CNN and YOHO. On the CPU, the prediction time of YOHO was 14 times faster than the CNN and 5 times faster than the CRNN. On the Graphical Processing Unit (GPU), the prediction time of YOHO was 6 times faster than the CNN and 4 times faster than the CRNN. The increase in prediction speed is because YOHO has to predict only 26 \(\times\) 6 neurons, whereas the CNN and CRNN have to predict 801 \(\times\) 2 neurons. Despite the CRNN having fewer parameters, YOHO is significantly faster.  
As YOHO outputs outputs acoustic boundaries directly, the post-processing and smoothing for YOHO was 7 times faster than the CNN and CRNN. Note that the smoothing is performed only on the CPU.  
Figure 4: Average time taken to make predictions on 1 h of audio for music-speech detection. ’Prediction’ refers to the time taken by the network to make predictions. ’Smoothing’ is the post-processing step to parse the output of the network. The GPU used for inference was the Tesla P100.  
Figure 3: Segment-based F-measures for each class on the Urban-SED dataset calculated using segment-size of 1 s.