# New Advances in Audio Signal Processing
## 1 Overview of Published Articles
A starting prototype employing a 128-microphone array spanning 50 by 50 cm\({}^{2}\), along with beamforming techniques, for real-time sound visualization is explored. Through simulations, to reduce the array size without compromising noise and distortion, it is shown that a strategically placed array of 32 microphones, approximately 20 by 20 cm\({}^{2}\), can surpass the larger prototype's performance in terms of directionality and noise cancellation for frequencies under 4 kHz, achieving a fourfold reduction in area with manageable compromises. The portability of these downsized acoustic imaging devices will broaden their utility in vehicle surveillance, urban soundscapes, and various industrial applications that are currently constrained by the larger sizes of traditional systems. Staying within the realm of sound field analysis, Contribution 9 delves into enhancing personal audio systems with sound field control technology, with specific attention paid to the physical properties of sound such as particle velocity. The authors [PERSON] and [PERSON] introduce a novel sound field control strategy that aims to minimize reconstruction error in the bright zone, reduce loudspeaker array exertion, and manage particle velocity and sound pressure in the dark zone. Utilizing a setup of five irregularly placed loudspeakers for computer simulations, they benchmark their method against established techniquessuch as pressure matching (PM), the eigen decomposition pseudoinverse method (EDPM), and acoustic contrast control (ACC), proving that their method lowers the loudspeaker array's effort and surpasses those of PM and EDPM in the bright zone's acoustic contrast index and outperforms ACC in reducing reconstruction errors. Specifically, the average effort of their array is substantially lower, achieving reductions of 9.4790 dB, 8.0712 dB, and 4.8176 dB against the ACC, PM, and EDPM methods, respectively, indicating that their approach provides the most stable reconstruction system, especially with non-uniform loudspeaker placements. This efficiency underscores the potential of their approach to significantly improve personal audio systems by ensuring more stable and efficient sound field reconstruction in setups where loudspeakers are unevenly distributed. Finally, [PERSON] et al. in Contribution 10 propose a CNN classifier based on scalograms for audio classification in construction sites. CNNs are a common solution for audio classification, and they are usually compared to more traditional machine learning solutions [19]. The scalogram is defined as the square modulus of the Continuous Wavelet Transform (CWT) [20], and here it is compared with the spectrogram, the most common solution for CNN-based audio analysis. Experimental results obtained using real-world sounds recorded at construction sites demonstrate state-of-the-art performances for their CNN + scalogram proposal, with a net based on AlexNet, compared with common architectures that employ spectrograms, demonstrating the effectiveness and completeness of the scalogram as a source of information for CNN-based sound analysis.