To simulate microphone signals from a moving sound source, the continuous trajectory of the source must be discretized into densely-distributed locations.

In speech separation, time-domain approaches have successfully replaced the time-frequency domain with latent sequence feature from a learnable encoder.

Neural Information Processing Systems http://nips.cc/

The ability of humans to separate and localize sounds in noisy environments is a remarkable phenomenon known as the "cocktail party effect."

Language models are prone to dataset biases, known as shortcuts and spurious correlations in data, which often result in performance drop on new data. We present a new debiasing framework called ``FairFlow'' that mitigates dataset biases by learning to be undecided in its predictions for data samples or representations associated with known or unknown biases. The framework introduces two key components: a suite of data and model perturbation operations that generate different biased views of input samples, and a contrastive objective that learns debiased and robust representations from the resulting biased views of samples. Experiments show that FairFlow outperforms existing debiasing methods, particularly against out-of-domain and hard test samples without compromising the in-domain performance

NDERWATER acoustic target recognition (UATR) is crucial for applications such as environmental monitoring, Deep learning models, such as convolutional neural networks exploration, and ship noise characterization, aiding in (CNNs), excel in feature representation and transfer learning, marine resource management and ocean-based technologies to adapting well to underwater acoustics when pre-trained on enhance ocean monitoring [1], [2]. Passive sonar uses external large vision datasets [11]-[13]. Similarly, pre-trained audio acoustic signals to identify underwater objects without emitting neural networks (PANNs) [14], trained on a large audio dataset sound [2]. Spectrograms, generated through signal processing (AudioSet [15]), have proven effective for passive sonar techniques like Short-Time Fourier Transform (STFT) classification where data scarcity is a challenge [16]. Moreover, and Mel-frequency spectrograms, transform signals into visual transformer-based models, including vision transformers representations, facilitating complex pattern extraction from (ViTs) [17] and audio spectrogram transformers (ASTs) [18], acoustic data [3]-[5].