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Sound source localization method based time-domain signal feature using deep learning
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DOI：10.1016/j.apacoust.2023.109626.png)
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Deep learning, as the most commonly used machine learning algorithm, is widely used in various fields. In the field of acoustics, deep learning methods are combined with frequency-domain features of signals to locate sound sources. The commonly frequency domain features include microphones array Cross-spectral-Matrix(CSM) and Short Time Fourier Transform(STFT). However, the use of frequency-domain features often leads to the loss of partial signal information and increases the computational complexity. This paper proposed a novel sound source localization algorithm based on time-domain features, which uses convolutional neural network(CNN) as a medium to achieve mapping from time-domain features to sound source locations. This method does not rely on any basic signal processing algorithm, and directly uses time-domain sampling points as network inputs for sound source localization. The application simulation shows that the proposed method can achieve precise localization and low side-lobe effect under different testing conditions. Once the network training is completed, the testing accuracy under different conditions is above 95%, with a maximum of 100%.
Keyword:
Sound source localization
Microphone array
Time-domain features
Convolutional nerual network
期刊
IF:
3.6
论文数:
7.4K
被引数:
1.7W
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