Sound Source Distance Estimation Using Deep Learning: An Image Classification Approach

Sensors

EunJoo Rhee¹  Mariam Yiwere¹ 

[1] Department of Computer Engineering, Hanbat National University, Daejeon 34158, Korea;

关键词: sound source distance estimation;    log-scaled mel spectrogram;    deep learning;    convolutional recurrent neural network;   

DOI  :  10.3390/s20010172

来源: DOAJ

【 摘 要 】

This paper presents a sound source distance estimation (SSDE) method using a convolutional recurrent neural network (CRNN). We approach the sound source distance estimation task as an image classification problem, and we aim to classify a given audio signal into one of three predefined distance classes—one meter, two meters, and three meters—irrespective of its orientation angle. For the purpose of training, we create a dataset by recording audio signals at the three different distances and three angles in different rooms. The CRNN is trained using time-frequency representations of the audio signals. Specifically, we transform the audio signals into log-scaled mel spectrograms, allowing the convolutional layers to extract the appropriate features required for the classification. When trained and tested with combined datasets from all rooms, the proposed model exhibits high classification accuracies; however, training and testing the model in separate rooms results in lower accuracies, indicating that further study is required to improve the method’s generalization ability. Our experimental results demonstrate that it is possible to estimate sound source distances in known environments by classification using the log-scaled mel spectrogram.

【 授权许可】

Unknown