Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

This paper presents noise type/position classification of various impact noises generated in a building which is a serious conflict issue in apartment complexes.
For this study, a collection of floor impact noise dataset is recorded with a single microphone.
Noise types/positions are selected based on a report by the Floor Management Center under Korea Environmental Corporation.
Using a convolutional neural networks based classifier, the impact noise signals converted to log-scaled Mel-spectrograms are classified into noise types or positions.
Also, our model is evaluated on a standard environmental sound dataset ESC-50 to show extensibility on environmental sound classification.

Some conflicts between residents originated from incorrect source localization by human hearing.
Also, correctly identifying noise types/locations is the first step for the noise reduction.
Therefore, noise type/position classification is a technique required to identify impact noise.Various impact noises such as footstep and hammer hitting in a living space incur annoyance to residents BID17 .
Chronic noise in a living space is a significant threat to resident's health BID18 BID15 .
In some case, impact noise arises conflict between residents.
Since more than 60 % of the residential buildings in Korea are apartment housings BID24 , the conflict has become serious social issue BID11 BID17 .
In 2012, the Korea government established the Floor Noise Management Center under Korea Environment Corporation affiliated with the Ministry of Environment BID4 for impact noise identification and conflict mediation.
The center has handled 119,500 civil complaints of impact noise over 6 years BID5 ).There
are several related works on noise reduction BID2 BID12 , annoyance measurement BID17 , and noise measurement BID7 BID18 . However
, impact noise classification is studied only in our previous work (Anonymous-authors, 2018) . Our previous
work studies classification of the impact noises using a convolutional neural networks (CNN) based model. Our model classifies
impact noise recordings into labeled categories. It shows extensibility
of CNN to impact noise classification. But, our model is evaluated
on the limited data generated on limited positions. And, the labels of dataset
are categorized into noise type-position combined form.In order to improve our previous work, this study expands the previous work as follows. First, 1, 000 impact noise
data is newly gathered on 10 more positions in the building. The new data is set as test
set to validate robustness of our model. Second, the classification
problem is divided into following two problems: noise type classification problem and position classification problem. This form is considered as
more adequate problem definition. Also, the number of samples
per category is increased which is expected to improve performance of our model. Third, our model is validated
on a standard environment sound dataset. This validation can show the
extensibility of our model to other problems.We expect that this work can contribute to other fields. Expected fields are noise type/position
classification in a very complex structure, and environmental sound classification.
In this study, a convolutional neural networks based model is proposed for noise type/position classification of impact noise.
An impact noise dataset is built for evaluation of our model.
The dataset is built based on a report by the Floor Management Center.
The dataset is divided into a trainingvalidation set and a test set.
The models for noise type and position classifications are separately designed, but their architectures are fundamentally same except the dimension of the adaptation layers.
VGG16 with an adaptation layer is employed for the tasks instead of designing a new model.
Since the impact noise dataset is small, parameters of VGG16 pre-trained on ImageNet are transferred to a model.
is the best accuracy ever reported on ESC-50 repository.
The result shows potential of the method to environmental sound classification as well as impact noise classification.Future works include impact noise generation at other buildings and apartment houses, and evaluation of the model on another standard environmental sound dataset.
This paper presents noise type/position classification of various impact noises generated in a building which is a serious conflict issue in apartment complexes