FS1-EcoAcousticAlarmDetection is a few-shot learning model designed to classify ecological audio recordings into seven categories: alarm, non-alarm, background, insect call, rain, low frequency sounds and high frequency sounds. It can also be used for classification into three categories: alarm, non alarm and background. The model begins by converting MP3 or WAV files into Mel spectrograms and, for each episode, randomly splits samples into a support set (5 samples per class), query set (6 samples per class), and test set (20 samples per class). Using an episodic batch sampler, 100 training episodes are generated. A CNN encoder with four convolutional blocks extracts embeddings from spectrograms, optimized via the Adam optimizer and cross-entropy loss. These embeddings are used by a Prototypical Network, which computes class prototypes from the support set and compares them to query embeddings using Euclidean distance, converting distances into log-probabilities for classification. A Relation Network made of fully connected layers (256 -> 128 -> 64 -> 1) takes concatenated embeddings of each query and prototype pair to compute similarity scores, optimized using MSE (mean squared error) loss. During evaluation, the model processes the test set over 100 episodes, extracting embeddings and producing final predictions using a weighted combination of prototypical probabilities (60%) and relation similarities (40%).
This model achieved an F1 of 98.1 on unseen samples.
This model flattens both dimensions, unlike FS 2 which maintains temporal structure by applying a pooling layer that compresses the frequency dimension into four representative bins to preserve the time axis. Compared to FS2, itt does not utilize an attention mechanism.
Compared to FS3, this model does not use temperature scaling (linear decay) to compare query embeddings with class prototypes. It flattens both dimensions, unlike FS3 which maintains temporal structure by applying a pooling layer that compresses the frequency dimension into four representative bins to preserve the time axis. It does not utilize an attention mechanism.