# New Advances in Audio Signal Processing
## 1 Overview of Published Articles
The subsequent experiment is designed to examine the potential interaction between loudness and sharpness in affecting annoyance, using listening tests with specifically chosen values of loudness and sharpness that reflect the ranges identified in the first experiment. Although no significant interaction between loudness and sharpness is detected, each factor individually shows a strong positive correlation with annoyance. Contribution 5 by [PERSON] et al. proposes a more efficient DL-based method for incomplete sound event detection (SED), specifically designed for situations where the training data only include the types of events, without precise timing. The authors propose an alternative weakly supervised architecture based on the U-Net [14], introducing a variant with con strained upsampling (LUU-Net) and global threshold average pooling (GTAP), aiming to diminish both the model's size and computational demands. This approach notably limits the increase in the frequency dimension in the U-Net's decoding phase, thereby shrinking the size of the output maps by almost 40% without causing a decrease in performances, as confirmed by experiments on a composite dataset from the DCASE 2018 Challenges 1 and 2 [15], showing how LUU-Net + GTAP trains 23% faster and scores higher in F1 metrics, achieving 0.644 for audio tagging and 0.531 for weakly supervised SED tasks, compared to the traditional approach. The principal advantage of the proposed LUU-Net lies in its efficiency, further enhanced by GTAP accelerating training and offering audio adaptability through the adjustment of a single hyperparameter. [PERSON] et al. in Contribution 6 tackle the task of analyzing dolphin whistles through advanced deep learning technologies for monitoring marine environments through echo-bioacoustics [16]. In their experimentations, they also explore the effects of applying data augmentation on the test dataset rather than the training dataset, augmenting test spectrograms with random shift with black or wrap and symmetric alternating diagonal shift. They employed an ensemble of 10 ResNet50 networks merged with the sum rule on a well-known public benchmark, obtaining notable improvements in performance across various established metrics, including a state-of-the-art accuracy rate of 94.9%. While further studies are essential to verify the effectiveness of these methods in diverse marine settings and for different species, deep learning is assessed as a very promising technique for creating and implementing cost-effective, automatic marine monitoring systems. Pitch detection is the task of identifying a sound's fundamental frequency (F0) and is applicable in many industrial fields, including music and fault detection, with different characteristics being sought from the algorithms, especially in terms of speed/real-time vs. accuracy. Contribution 7 by [PERSON] et al. proposes a novel, high-speed pitch detection algorithm called OBP (One Bit Pitch) to realize the fastest possible pitch detection, suitable for applications needing real-time tracking. It is implemented using a bitwise variant of the autocorrelation method used for estimating F0 [17] and operated on a synthetic dataset of signals with a known pitch. OBP is compared to the state-of-the-art algorithms for all other applications, including a deep learning-based one, and demonstrates higher speed than all of them, being nine times faster than the previous fastest one, with a mean elapsed time of 0.046 \(\times\) real time. Despite being less accurate for high-precision landmarks and noisy signals, its performances are still within the realm of acceptability (<2%) and comparable to standards like YIN [18]. [PERSON]'s Contribution 8 deals with acoustic imaging for spatially mapping sounds: it is based on heavy \"acoustic cameras\" comprising spatially localized microphone arrays, and the study explores techniques for shrinking their size and enhancing their portability.