# New Advances in Audio Signal Processing
## 1 Overview of Published Articles  
[PERSON] et al. (Contribution 1) developed an experimental audio-based approach to evaluate the effects of noise from gas discharges, comparing different nozzle types undera variety of conditions and linking these effects to design features. The use of nozzles in gas-based fire suppression systems within data centers can produce sound levels that significantly degrade hard disk performance; to mitigate this issue, silent nozzles are utilized. Both standard and silent nozzles are analyzed during the release of inert gases and halocarbon compounds. The effectiveness of silent nozzles in maintaining noise levels below the critical 110 dB threshold is confirmed. The findings suggest a correlation between the Reynolds number and the peak noise levels [8], indicating that noise increases with higher flow rates. Slower hard drives were particularly vulnerable. A spectral analysis revealed that higher frequency noises could impair performance, even when below the set threshold. Additionally, the study found that the type of fire suppressant used did not affect noise levels relative to the volumetric flow rate; however, denser gases produced less noise for the same mass flow rate. Contribution 2 by [PERSON] et al. underlines another aspect of audio signal analysis by focusing on the deep learning-based detection of voice pathology; in this case, it is COVID-19. The authors propose a novel set of features tailored for COVID detection, comprising MFCC, \(\Delta\)2-MFCC, \(\Delta\)-MFCC [9], and spectral contrast, and integrate these features into a CNN architecture fusing a ResNet-50 [10] to a custom DNN. The proposed model + feature pairing, trained using selected cough samples sourced from public databases including Cambridge [11], Coswara [12], and COUGHYID [13], results in a 96% accuracy rate. [PERSON] et al. (Contribution 3) explore a novel approach for estimating the impact of room acoustics, especially reverberation, on the intelligibility and information content of sound signals. This is achieved by training recurrent neural networks using both echo-free and echo-rich audio samples to build an estimator of the room impulse response. The models are trained on a newly created synthetic dataset carefully adjusted to match the characteristics of actual recorded impulse responses. Given the complexity and practical challenges involved in simulating especially long room impulse responses at 44.1 kHz using traditional time-domain methods, they propose a frequency domain-based strategy enhanced by a logarithmic band re-weighing of the auditory spectrum. Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU) are compared to evaluate the model's ability to accurately predict the spectral characteristics of reverberated sound across adjacent frequency bands. The paper proves how different frequency bands have relevant impacts on the effect of reverberation, all the while determining the BiLSTM to be the most flexible network for estimating the room impulse response. [PERSON] et al. (Contribution 4) also considered the analysis of sound for evaluating the performance of another system, aiming to assess the variations in both the signal and the psychoacoustic properties of canister-type vacuum cleaners. Fifteen vacuum cleaners were selected to evenly cover a broad range of reported sound power levels, and their differences in acoustic and psychoacoustic parameters were analysed to identify common noise characteristics, aiming to finding a prototypical noise for canister applications. The second objective of this study is to learn how the noise from vacuum cleaners is perceived as annoying by listeners. This is achieved via two experiments, the first of which aimed to identify which factors contribute to the perception of annoyance caused by vacuum cleaner noise. Loudness, sharpness, and the presence of tonal components at both low and high frequencies are the primary factors influencing annoyance ratings.