In this challenge, we replicate the DCASE challenge for the scale of the AML course.

## Context

Quoting from the official [DCASE challenge website](https://dcase.community/challenge2020/task-unsupervised-detection-of-anomalous-sounds):

> **Anomalous sound detection (ASD) is the task to identify whether the sound emitted from a target machine is normal or anomalous**. Automatically detecting mechanical failure is an essential technology in the fourth industrial revolution, including artificial intelligence (AI)-based factory automation. Prompt detection of machine anomaly by observing its sounds may be useful for machine condition monitoring.

> **The main challenge of this task is to detect unknown anomalous sounds under the condition that only normal sound samples have been provided as training data.** In real-world factories, actual anomalous sounds rarely occur and are highly diverse. Therefore, exhaustive patterns of anomalous sounds are impossible to deliberately make and/or collect. This means we have to detect unknown anomalous sounds that were not observed in the given training data. This point is one of the major differences in premise between ASD for industrial equipment and the past supervised DCASE tasks for detecting defined anomalous sounds such as gunshots or a baby crying.

## Your Objective

Given normal sound samples of some machine IDs (each sound has information about its machine ID), we want to build a model which can be used to predict whether an unknown sound (this unknown sound belongs to one of the given machine IDs in the training dataset) is normal or anomalous.