# Weakly Supervised U-Net with Limited Upsampling for Sound Event Detection
## 4 Experiments
### Dataset Generation  
According to the data augmentation in previous work [16], we built a large SED training dataset from audio tagging samples by synthesizing several audio clips with white noise or other types of audio sounds as background noise sounds. This framework is regarded as one of the data augmentation methods, allowing the generation of the training dataset with the various choices of different sounds. When adding a number of different sounds, their onset times and clipping lengths are varied to simulate various overlapping cases in real situations. Figure 6 shows the procedures of training data generation. The audio sounds are represented by 2D time-frequency spectrograms. There are many combinations of onset times and clipping lengths and how much audio is in a single recording. These combinations help the trained model work well with various SED tasks.  
Audio classification experiments were carried out on 2 different datasets to evaluate the proposed method. DCASE 2018 Task 1 is a dataset consisting of a total of 8640 audio samples [32]. Each of them is 10 s long and sampled at 48 kHz. We used it as background sounds in evaluation data synthesis. The DCASE 2018 Task 2 is an audio tagging task [33]. The tagging dataset consists of about 9500 training samples of 41 categories, which are distributed unequally and sampled at 44.1 kHz. The smallest category has 94 samples and the largest 300 samples.  
We adopted the policy suggested by the DCASE Challenge Guidelines [16] to generate the training dataset. In the original policy suggested by the DCASE Challenge, 3 distinct audio files were chosen from DCASE Task 2, clipped up to 2 s, and combined with additional background sounds to generate samples for training. The onset times were 0.5 s, 3 s, and 5.5 s, so there was no overlap among the 3 sound events. Besides the original policy, we performed several different synthetic polices: random onset, longer clipping, and mixed policies. Figure 7 shows the generated sample according to each policy. The longer clipping policy uses the maximum clip length of 5 s, which is longer than the 2 s suggested by the original guideline. This policy has a high probability of generating a sample containing overlapping events. In the random onset policy, the onsets of the events are randomly chosen from \([0.5,6.5)\). This policy has a smaller probability of generating a sample containing overlapping events than the longer clipping policy, but in this sample, events can start at any time. The mixed policy uses both the random onset and longer clipping, making the generated sample very unpredictable. These policies are summarized in Table 1.  
Figure 6: Training data generation procedure. Two audio samples are mixed with a background sound. Audio samples are clipped to a given length, normalized, and then mixed with normalized background noise. The \(x\)-axis is time in seconds, and for the \(y\)-axis frequency bin index, the larger the higher it is.  
Figure 7: Comparison of temporal overlaps of different mixing polices. (**a**) Original policy with no overlap; (**b**) longer clipping policy allowing some overlaps between events by using longer clips; (**c**) random onset policy allowing events to start at any time; some samples overlap, but some other samples do not due to the randomness; (**d**) mixed policies in (**b,c**). Most overlaps are observed.