## Audio dataset

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

> The data used for this task comprises parts of ToyADMOS and the MIMII Dataset consisting of the normal/anomalous operating sounds of six types of toy/real machines. Each recording is a single-channel (approximately) 10-sec length audio that includes both a target machine's operating sound and environmental noise. The following six types of toy/real machines are used in this task:

> - Toy-car (ToyADMOS)
- Toy-conveyor (ToyADMOS)
- Valve (MIMII Dataset)
- Pump (MIMII Dataset)
- Fan (MIMII Dataset)
- Slide rail (MIMII Dataset)

To make this challenge to be suitable for the scale of the AML course, we will work on only one machine type (i.e. Slide rail). 

## Recording procedure
> The ToyADMOS consists of normal/anomalous operating sounds of miniature machines (toys) collected with four microphones, and the MIMII dataset consists of those of real-machines collected with eight microphones. Anomalous sounds in these datasets were collected by deliberately damaging target machines. For simplifying the task, we used only the first channel of multi-channel recordings; all recordings are regarded as single-channel recordings of a fixed microphone. The sampling rate of all signals has been downsampled to 16 kHz. From ToyADMOS, we used only IND-type data that contain the operating sounds of the entire operation (i.e., from start to stop) in a recording. We mixed a target machine sound with environmental noise, and only noisy recordings are provided as training/test data. The environmental noise samples were recorded in several real factory environments. For the details of the recording procedure, please refer to the papers of [ToyADMOS](https://ieeexplore.ieee.org/document/8937164) and [MIMII Dataset](https://dcase.community/documents/workshop2019/proceedings/DCASE2019Workshop_Purohit_21.pdf).

## Development and evaluation datasets
> We first define two important terms in this task: Machine Type and Machine ID. Machine Type means the kind of machine, which in this task can be one of six: toy-car, toy-conveyor, valve, pump, fan, and slide rail. Machine ID is the identifier of each individual of the same type of machine, which in the training dataset can be of three or four.

> Figure 2 shows an overview of our dataset, which consists of a development dataset, an additional training dataset, and an evaluation dataset.

> - **Development dataset**: Each Machine Type has three or four Machine IDs. Each machine ID's dataset consists of (i) around 1,000 samples of normal sounds for training and (ii) 100-200 samples each of normal and anomalous sounds for the test. The normal and anomalous sound samples in (ii) are only for checking performance therefore the sound samples in (ii) shall not be used for training.
- **Evaluation dataset**: This dataset consists of the same Machine Types' test samples as the development dataset. The number of test samples for each Machine ID is around 400, none of which have a condition label (i.e., normal or anomaly). Note that the Machine IDs of the evaluation dataset are different from those of the development dataset.
- **Additional training dataset**: This dataset includes around 1,000 normal samples for each Machine Type and Machine ID used in the evaluation dataset. The participants can also use this dataset for training.

![](https://github.com/tuananh0305/DCASE_2020_task2_baseline_pytorch/blob/master/imgs/img1.png?raw=true)

**Note that** the Machine IDs used in the development dataset are different from those used in the evaluation dataset to avoid leaking label information during the training. The **Development dataset** is used to develop a model and tune hyper-parameters. Then, the model should be re-trained on the **Additional training dataset** before making predictions on the **Evaluation dataset**.