# You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection
## 3 Datasets
### TUT Sound Event Detection  
The TUT Sound Event Detection dataset focuses on environmental sound detection [28]. It was adopted for the third task of the DCASE challenge 2017. It primarily consists of street recordings with traffic and other activity. Each audio example is 2.56 s. There were six unique audio classes--Brakes Squeaking, Car, Children, Large Vehicle, People Speaking, and People Walking. Thus, to predict the existence of the six classes, plus start and end times, we required 18 output neurons. The more recent DCASE challenges use additional techniques such as semi-supervised learning and source separation, which is not the focus of this study. Hence, we used the dataset from 2017 that contains only strongly labeled data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset Division** & **Contents** \\ \hline Train & 46 h of synthetic radio data + 9 h from BBC Radio Devon + 1 h 30 min from \\  & MuSpeak \\ \hline Validation & 5 h from BBC Radio Devon + 2 h from MuSpeak \\ \hline Test & 4 h from BBC Radio Devon + 1 h 42 min from MuSpeak \\ \hline \hline \end{tabular}
\end{table}
Table 4: Contents of train, validation, and test datasets for music-speech detection. Real-world radio data was collected from BBC Radio Devon and annotated by the authors. MuSpeak [38] already contains annotations for music and speech. 46 h of artificial radio-like examples were synthesised by the method presented in this study [17].  
The total size of the dataset is approximately 1.5 h. The dataset comes with a four-fold cross-validation setup. The size of this dataset is significantly smaller than the one used for music-speech detection and may not be large enough for our deep learning architecture. Therefore, we applied L2 normalization of 0.001 on the first Conv2D layer. In addition, we included L2 normalization of 0.01 and spatial dropout of 0.1 on all the subsequent Conv2D layers. For data augmentation, we incorporated SpecAugment [36], which randomly drops a sequence of frequency bins or time steps from the input. Note that there were slight differences in our implementation of SpecAugment. We did not use any time warping because it becomes complicated to redefine labels for audio events. In addition, we applied SpecAugment on batches instead of individual examples to save computational time.  
The database contained stereo audio files with a sampling rate of 44.1 kHz. These were downmixed to mono before pre-processing. Subsequently, we extracted 40 log-mel bands in the range of 0 to 22,050 Hz. The hop size was 10 ms and the window size was 40 ms. We adopted audio features similar to the baseline system [28] for the task, except that we used a smaller hop size. As the input of the network contains 2.56 s of audio, the input shape is 257 \(\times\) 40 corresponding to 257 times steps and 40 mel bins. The output shape of the network is 9 \(\times\) 18, corresponding to 9 times steps and 6 acoustic classes. Note that each time step in this case is 0.284 s, which is different from 0.307 s for music-speech detection. In both cases, we used the same network and the same sequence of convolutional layers. The convolutions with a stride of 2 reduces the temporal dimension by half. Hence, due to different input sizes, the number of time steps is 9 in one case and 26 in the other case. There were no special measures taken to estimate the duration of each time step beforehand. However, in most cases it was somewhere around 0.3 s, due to the hop and window sizes selected for feature extraction.