# 6th place solution

Thanks to Kaggle, the hosts, and all the participants. It was a great opportunity for me to learn deep learning and audio signal processing. Shout out to
[@hidehisaarai1213](https://www.kaggle.com/hidehisaarai1213)
and
[@taggatle](https://www.kaggle.com/taggatle)
(previous 1st place solution) for sharing. I build my solution based on their SED model. I learned a lot and I'm really thankful to them.
Model
The logmelspectrograms are calculated with n_fft=1024, sr=32000, hop_length=320, n_mels=64. I used the SED model with DenseNet backbone. Training and inference are performed on 30-second clips. Secondary labels are treated the same as the primary label. The loss function is BCEloss.
Augmentation
The waveforms are augmented with Gaussian Noise, Gaussian SNR, Gain, Pink Noise, some environment noise (rain, insect, etc), and nocall segments from training soundscapes. I also used SpecAugment and mixup.
Threshold and Ensemble
For each model, I did a grid search on the threshold. Then I used voting to ensemble the models. I chose the thresholds that give higher recall and the vote limit that gives the best f1 score, all based on the training soundscapes. Restricting the prediction using the geographic location also improved the score a little bit.