A Study of Features and Deep Neural Network Architectures and Hyper-Parameters for Domestic Audio Classification  
[PERSON]  
1 Faculty of Engineering and Information Sciences, University of Wollongong in Dubai,  
Dubai 20183, United Arab Emirates, [EMAIL_ADDRESS] (A.C.); [EMAIL_ADDRESS]. audio signals containing acoustic scenes, which is required to understand the continuous nature of daily domestic activities. Acoustic scenes refer to the sound scene recording of a certain activity over time, while sound events refer to more specific sound classes happening at short periods of time within a duration [5]. The detection of multi-channel audio was also found to be 10% more accurate when compared to single channel audio, considering the case of overlapping sounds that commonly occur in real-life [6]. Such overlapping sounds may be better detected through joint processing from different channels, reducing the effects of background noise and other interference. A similar concept to this work is the Detection and Classification of Acoustic Scenes and Events (DCASE) 2018 Task 5 challenge, which focuses on domestic multi-channel acoustic scene classification [7]. In this challenge, top performing methods often involve the use of Log-Mel energies and Mel-frequency Cepstral Coefficients (MFCC), while VGG-16 and VGG-ish pre-trained models are common choices for classification. The use of Log-Mel continues to be a popular choice for features in top performing methods of the DCASE 2019 and 2020 Task 4 challenges on sound event detection and classification [7]. Nonetheless, the utilization of spectro-temporal scalograms for multi-channel classification has not yet been thoroughly explored. Log-Mel energies are a subset of spectral features, which consider the frequency components of a signal [8]. On the other hand, MFCCs are based on the cepstral representation of a signal, which results from the Inverse Fourier Transform (IFT) of the spectral components of the signal [8]. Although these algorithms are commonly used and are popular for noise-free environments, they have several challenges when faced in noisy acoustic environments [8; 9]. Hence, this work aims to determine the optimum feature for domestic multi-channel acoustic scene classification, which takes into account real-life scenarios, such as the presence of different types of background noise. Although the DCASE 2018 Task 5 challenge had real recordings in real environments, the specific characteristics of the noise and reverberation were unknown. Hence, here we conduct a controlled study on these effects using a new database with known characteristics. Experimentation is done by conducting a thorough analysis and comparison of the classification performances and processing time of cepstral and spectral features for several pre-trained neural network and compact neural network models, using weight-sensitive metrics. It is important to note that the use of weight-sensitive metrics is important, in order to take into account the biasing that may be caused by imbalanced datasets. Further, a study on the effects of architectural and hyper-parameter modification on the optimum pre-trained network has also been looked into, in order to reduce the size of the network while maintaining its performance. In turn, we propose the use of spectro-temporal features in the form of scalograms, which are computed through a fast Fourier transform (FFT)-based continuous wavelet transform (CWT) [10]. These features possess excellent time and frequency localization, allowing a thorough representation of continuous signals with minimal loss of information [10]. This is coupled with a modified AlexNet Model, which consists of 33 layers instead of 25, and utilizes a leaky rectified linear unit (ReLU) activation function instead of a traditional ReLU function. Finally, we also synthesize an original database, which aims to recreate scenarios that could occur in real life, in order to test and verify the overall robustness of the system.