Single-channel blind source separation of underwater acoustic signals using improved NMF and FastICA

Li, Dawei; Wu, Minghui; Yu, Liang; Han, Je-Chin; Zhang, Hao

doi:10.3389/fmars.2022.1097003

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…Many scholars have published methods and validations in the field of underwater acoustic BSS. For conventional algorithms, such as in [32], the researchers build an improved non-negative matrix factorization (NMF)-based BSS algorithm on a fast independent component analysis (FastICA) machine learning backbone to obtain a better signal-to-noise reduction and separation accuracy. Furthermore, a low-complexity method based on Probabilistic Stone's Blind Source Separation (PS-BSS) is proposed in [33] to be used in multi-input multi-output (MIMO) orthogonal frequency division modulation (OFDM) in the Internet of Underwater Things (IoUT).…”

Section: Introductionmentioning

confidence: 99%

Underwater Acoustic Nonlinear Blind Ship Noise Separation Using Recurrent Attention Neural Networks

Song,

Feng,

Wang

et al. 2024

Remote Sensing

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Ship-radiated noise is the main basis for ship detection in underwater acoustic environments. Due to the increasing human activity in the ocean, the captured ship noise is usually mixed with or covered by other signals or noise. On the other hand, due to the softening effect of bubbles in the water generated by ships, ship noise undergoes non-negligible nonlinear distortion. To mitigate the nonlinear distortion and separate the target ship noise, blind source separation (BSS) becomes a promising solution. However, underwater acoustic nonlinear models are seldom used in research for nonlinear BSS. This paper is based on the hypothesis that the recovery and separation accuracy can be improved by considering this nonlinear effect in the underwater environment. The purpose of this research is to explore and discover a method with the above advantages. In this paper, a model is used in underwater BSS to describe the nonlinear impact of the softening effect of bubbles on ship noise. To separate the target ship-radiated noise from the nonlinear mixtures, an end-to-end network combining an attention mechanism and bidirectional long short-term memory (Bi-LSTM) recurrent neural network is proposed. Ship noise from the database ShipsEar and line spectrum signals are used in the simulation. The simulation results show that, compared with several recent neural networks used for linear and nonlinear BSS, the proposed scheme has an advantage in terms of the mean square error, correlation coefficient and signal-to-distortion ratio.

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Section: Introductionmentioning

confidence: 99%

Underwater Acoustic Nonlinear Blind Ship Noise Separation Using Recurrent Attention Neural Networks

Song,

Feng,

Wang

et al. 2024

Remote Sensing

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The rise of nonnegative matrix factorization: Algorithms and applications

Guo,

Li,

Liang

2024

Information Systems

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A Robust Hybrid Neural Network Architecture for Blind Source Separation of Speech Signals Exploiting Deep Learning

Ansari,

Alnajjar,

Khater

et al. 2023

IEEE Access

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In the contemporary era, blind source separation has emerged as a highly appealing and significant research topic within the field of signal processing. The imperative for the integration of blind source separation techniques within the context of beyond fifth-generation and sixth-generation networks arises from the increasing demand for reliable and efficient communication systems that can effectively handle the challenges posed by high-density networks, dynamic interference environments, and the coexistence of diverse signal sources, thereby enabling enhanced signal extraction and separation for improved system performance. Particularly, audio processing presents a critical domain where the challenge lies in effectively handling files containing a mixture of human speech, silence, and music. Addressing this challenge, speech separation systems can be regarded as a specialized form of human speech recognition or audio signal classification systems that are leveraged to separate, identify, or delineate segments of audio signals encompassing human speech. In various applications such as volume reduction, quality enhancement, detection, and identification, the need arises to separate human speech by eliminating silence, music, or environmental noise from the audio signals. Consequently, the development of robust methods for accurate and efficient speech separation holds paramount importance in optimizing audio signal processing tasks. This study proposes a novel three-way neural network architecture that incorporates transfer learning, a pre-trained dual-path recurrent neural network, and a transformer. In addition to learning the time series associated with audio signals, this network possesses the unique capability of direct context-awareness for modeling the speech sequence within the transformer framework. A comprehensive array of simulations is meticulously conducted to evaluate the performance of the proposed model, which is benchmarked with seven prominent state-of-the-art deep learning-based architectures. The results obtained from these evaluations demonstrate notable advancements in multiple objective metrics. Specifically, our proposed solution showcases an average improvement of 4.60% in terms of short-time objective intelligibility, 14.84% in source-to-distortion ratio, and 9.87% in scale-invariant signal-to-noise ratio. These extraordinary advancements surpass those achieved by the nearest rival, namely the dual-path recurrent neural network time-domain audio separation network, firmly establishing the superiority of our proposed model's performance.

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Single-channel blind source separation of underwater acoustic signals using improved NMF and FastICA

Cited by 5 publications

References 24 publications

Underwater Acoustic Nonlinear Blind Ship Noise Separation Using Recurrent Attention Neural Networks

Underwater Acoustic Nonlinear Blind Ship Noise Separation Using Recurrent Attention Neural Networks

The rise of nonnegative matrix factorization: Algorithms and applications

A Robust Hybrid Neural Network Architecture for Blind Source Separation of Speech Signals Exploiting Deep Learning

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