# Application of Wavelet Transform for the Detection of Cetacean Acoustic Signals

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## Abstract

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## Featured Application

**We believe that this research will be helpful for the detection of Cetacean acoustic signals for research purposes or dataset building for the purpose of more accurate artificial neural network training.**

## Abstract

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Hydrophone Data

#### 2.2. Methods of Signal Analysis

#### 2.2.1. Manual Method

#### 2.2.2. STFT

#### 2.2.3. WT

#### 2.3. Target Signal Detection and Marking Experiment

## 3. Results

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 4.**Time–frequency spectra provided by STFT and WT. (

**a**) Spectrum calculated by STFT. (

**b**) Spectrum calculated by WT.

**Figure 6.**The time–frequency spectrum of clicks and knocking sounds. (

**a**) A knock sound at point A. (

**b**) A knock sound at point B. (

**c**) A knock sound at point C. (

**d**) A typical click signal.

Parameters | Value |
---|---|

Type | Sound Trap ST300 HF, Ocean Instruments |

Memory | 256 GB |

Frequency band | 20~144 kHz |

Sampling rate | 288 kHz |

Resolution | 16-bit |

Minimum self-noise | 37 dB |

Parameters | Setting |
---|---|

Software | SciPy 1.7.1 ^{1} |

Window | Hamming |

Window length | 500 sampling points |

Overlap | 250 sampling points |

^{1}A collection of mathematical algorithms (Software SciPy v1.7.1) [32]. Here, we used Python 3.9.7.

Parameters | Setting |
---|---|

Software | PyWavelets v1.1.1 ^{1} |

Base wavelet | Cmor100-100 |

Total scale | 1000 |

Sampling period | Decided by sample rate of original signal |

Scales | Calculated by Function (3) |

^{1}An open-source wavelet transform software (Software PyWavelets v1.1.1) [40]. Here, we used Python 3.9.7.

Target Signal | Parameters | Value |
---|---|---|

Whistle | ${T}_{\mathrm{e}}$ | 50,000 |

${f}_{\mathrm{m}\mathrm{i}\mathrm{n}}$ | 15 kHz | |

${f}_{\mathrm{m}\mathrm{a}\mathrm{x}}$ | 40 kHz | |

${M}_{\mathrm{t}\mathrm{m}\mathrm{i}\mathrm{n}}$ | 0.5 s | |

${M}_{\mathrm{t}\mathrm{m}\mathrm{a}\mathrm{x}}$ | 3 s | |

Burst pulse | ${T}_{\mathrm{e}}$ | 50,000 |

${f}_{\mathrm{m}\mathrm{i}\mathrm{n}}$ | 10 Hz | |

${f}_{\mathrm{m}\mathrm{a}\mathrm{x}}$ | 15 kHz | |

${M}_{\mathrm{t}\mathrm{m}\mathrm{i}\mathrm{n}}$ | 0.2 s | |

${M}_{\mathrm{t}\mathrm{m}\mathrm{a}\mathrm{x}}$ | 1 s | |

Click | ${T}_{\mathrm{e}}$ | 40,000 |

${f}_{\mathrm{m}\mathrm{i}\mathrm{n}}$ | 40 kHz | |

${f}_{\mathrm{m}\mathrm{a}\mathrm{x}}$ | 144 kHz | |

${M}_{\mathrm{t}\mathrm{m}\mathrm{i}\mathrm{n}}$ | 15 μs | |

${M}_{\mathrm{t}\mathrm{m}\mathrm{a}\mathrm{x}}$ | 35 μs |

Type of Signal | Manual | STFT | WT |
---|---|---|---|

Whistle | 1062 | 1062 | 1059 |

Burst pulse | 1361 | 1361 | 1352 |

Click | 2501 | 2382 | 3057 |

Parameters | Value |
---|---|

Type | SC2-ETH, Ocean Sonics |

Memory | 256 GB |

Frequency band | 10~128 kHz |

Sampling rate | 256 kHz |

Resolution | 16-bit |

Minimum self-noise | 27 dB |

Type of Signal | Knock Number | Manual | STFT | WT |
---|---|---|---|---|

A | 50 | 32 | 15 | 11 |

B | 50 | 50 | 50 | 4 |

C | 50 | 50 | 43 | 7 |

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**MDPI and ACS Style**

He, R.; Dai, Y.; Liu, S.; Yang, Y.; Wang, Y.; Fan, W.; Zhang, S.
Application of Wavelet Transform for the Detection of Cetacean Acoustic Signals. *Appl. Sci.* **2023**, *13*, 4521.
https://doi.org/10.3390/app13074521

**AMA Style**

He R, Dai Y, Liu S, Yang Y, Wang Y, Fan W, Zhang S.
Application of Wavelet Transform for the Detection of Cetacean Acoustic Signals. *Applied Sciences*. 2023; 13(7):4521.
https://doi.org/10.3390/app13074521

**Chicago/Turabian Style**

He, Ruilin, Yang Dai, Siyi Liu, Yuhao Yang, Yingdong Wang, Wei Fan, and Shengmao Zhang.
2023. "Application of Wavelet Transform for the Detection of Cetacean Acoustic Signals" *Applied Sciences* 13, no. 7: 4521.
https://doi.org/10.3390/app13074521