A Method of Blind Separation for the Single Vector Hydrophone

2017 ◽  
Author(s):  
Tong Zou ◽  
Ying Wang ◽  
Shuming Dong ◽  
Weihua Zhong
Keyword(s):  
Blind Separation ◽  
Vector Hydrophone

A Riemannian approach to blind separation of t-distributed sources

2021 ◽  
Author(s):  
Florent Bouchard ◽  
Arnaud Breloy ◽  
Guillaume Ginolhac ◽  
Alexandre Renaux
Keyword(s):  
Blind Separation

scholarly journals Design and implementation of a jellyfish otolith-inspired MEMS vector hydrophone for low-frequency detection

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Renxin Wang ◽  
Wei Shen ◽  
Wenjun Zhang ◽  
Jinlong Song ◽  
Nansong Li ◽  
...  
Keyword(s):  
Structural Parameters ◽  
Low Frequency ◽  
Directivity Pattern ◽  
Stress Distributions ◽  
Strong Response ◽  
Underwater Acoustic ◽  
Frequency Detection ◽  
Vector Hydrophone ◽  
Shock Resistance ◽  
Marine Applications

AbstractDetecting low-frequency underwater acoustic signals can be a challenge for marine applications. Inspired by the notably strong response of the auditory organs of pectis jellyfish to ultralow frequencies, a kind of otolith-inspired vector hydrophone (OVH) is developed, enabled by hollow buoyant spheres atop cilia. Full parametric analysis is performed to optimize the cilium structure in order to balance the resonance frequency and sensitivity. After the structural parameters of the OVH are determined, the stress distributions of various vector hydrophones are simulated and analyzed. The shock resistance of the OVH is also investigated. Finally, the OVH is fabricated and calibrated. The receiving sensitivity of the OVH is measured to be as high as −202.1 dB@100 Hz (0 dB@1 V/μPa), and the average equivalent pressure sensitivity over the frequency range of interest of the OVH reaches −173.8 dB when the frequency ranges from 20 to 200 Hz. The 3 dB polar width of the directivity pattern for the OVH is measured as 87°. Moreover, the OVH is demonstrated to operate under 10 MPa hydrostatic pressure. These results show that the OVH is promising in low-frequency underwater acoustic detection.

scholarly journals Sparse pursuit and dictionary learning for blind source separation in polyphonic music recordings

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Sören Schulze ◽  
Emily J. King
Keyword(s):  
Single Channel ◽  
Spectral Characteristics ◽  
Musical Instruments ◽  
Model Parameters ◽  
Blind Separation ◽  
Audio Signals ◽  
Frequency Spectra ◽  
Acoustic Instruments ◽  
Invariant Properties ◽  
Music Recordings

AbstractWe propose an algorithm for the blind separation of single-channel audio signals. It is based on a parametric model that describes the spectral properties of the sounds of musical instruments independently of pitch. We develop a novel sparse pursuit algorithm that can match the discrete frequency spectra from the recorded signal with the continuous spectra delivered by the model. We first use this algorithm to convert an STFT spectrogram from the recording into a novel form of log-frequency spectrogram whose resolution exceeds that of the mel spectrogram. We then make use of the pitch-invariant properties of that representation in order to identify the sounds of the instruments via the same sparse pursuit method. As the model parameters which characterize the musical instruments are not known beforehand, we train a dictionary that contains them, using a modified version of Adam. Applying the algorithm on various audio samples, we find that it is capable of producing high-quality separation results when the model assumptions are satisfied and the instruments are clearly distinguishable, but combinations of instruments with similar spectral characteristics pose a conceptual difficulty. While a key feature of the model is that it explicitly models inharmonicity, its presence can also still impede performance of the sparse pursuit algorithm. In general, due to its pitch-invariance, our method is especially suitable for dealing with spectra from acoustic instruments, requiring only a minimal number of hyperparameters to be preset. Additionally, we demonstrate that the dictionary that is constructed for one recording can be applied to a different recording with similar instruments without additional training.

scholarly journals A Direction-of-Arrival Estimation Algorithm Based on Compressed Sensing and Density-Based Spatial Clustering and Its Application in Signal Processing of MEMS Vector Hydrophone

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2191
Author(s):  
Huichao Yan ◽  
Ting Chen ◽  
Peng Wang ◽  
Linmei Zhang ◽  
Rong Cheng ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Spatial Clustering ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Cluster Center ◽  
Direction Of Arrival Estimation ◽  
Vector Hydrophone ◽  
Regularization Parameters ◽  
Selection Of

Direction of arrival (DOA) estimation has always been a hot topic for researchers. The complex and changeable environment makes it very challenging to estimate the DOA in a small snapshot and strong noise environment. The direction-of-arrival estimation method based on compressed sensing (CS) is a new method proposed in recent years. It has received widespread attention because it can realize the direction-of-arrival estimation under small snapshots. However, this method will cause serious distortion in a strong noise environment. To solve this problem, this paper proposes a DOA estimation algorithm based on the principle of CS and density-based spatial clustering (DBSCAN). First of all, in order to make the estimation accuracy higher, this paper selects a signal reconstruction strategy based on the basis pursuit de-noising (BPDN). In response to the challenge of the selection of regularization parameters in this strategy, the power spectrum entropy is proposed to characterize the noise intensity of the signal, so as to provide reasonable suggestions for the selection of regularization parameters; Then, this paper finds out that the DOA estimation based on the principle of CS will get a denser estimation near the real angle under the condition of small snapshots through analysis, so it is proposed to use a DBSCAN method to process the above data to obtain the final DOA estimate; Finally, calculate the cluster center value of each cluster, the number of clusters is the number of signal sources, and the cluster center value is the final DOA estimate. The proposed method is applied to the simulation experiment and the micro electro mechanical system (MEMS) vector hydrophone lake test experiment, and they are proved that the proposed method can obtain good results of DOA estimation under the conditions of small snapshots and low signal-to-noise ratio (SNR).

Design and Implementation of Beaded Cilia MEMS Vector Hydrophone

Measurement ◽  
2021 ◽  
pp. 109751
Author(s):  
Ting Lv ◽  
Xiaoqi Liang ◽  
Guojun Zhang ◽  
Shasha Yang ◽  
Peng Chen ◽  
...  
Keyword(s):  
Design And Implementation ◽  
Vector Hydrophone
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