Pallas: Novel Sound Classification at the Edge

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Sound pollution is becoming an increasingly pressing issue in today’s world. To effectively address it, it must be measured. To this end, Serval was developed, an edge-ai powered sound recognition solution. Its lack of accuracy, however, makes it difficult to deploy. This thesis examines the potential for improving this solution while staying within its technical limitations in order to raise the accuracy to satisfactory levels. Multiple aspects of Serval were evaluated and compared to the current stateof-the-art: its data augmentation, the embedding it uses, and the hardware it runs on. Alternatives for each of these components were evaluated and each aspect was optimized.
The results show that after these improvements, the single-label F1-score increased from 0.60 to 0.76, and the single- and multi-label combined F1-score increased from 0.64 to 0.67. Finally, power consumption has been reduced by 14%, partially thanks to the usage of specialized hardware. One issue that has yet to be adequately addressed is the size of the dataset. By increasing the number of samples, the accuracy could be further improved.