Audio signal processor providing simulated source distance control

Michael A. Gerzon

doi:10.1121/1.419787

Audio signal processor providing simulated source distance control

The Journal of the Acoustical Society of America ◽

10.1121/1.419787 ◽

1997 ◽

Vol 102 (1) ◽

pp. 18

Author(s):

Michael A. Gerzon

Keyword(s):

Audio Signal ◽

Distance Control ◽

Source Distance ◽

Simulated Source ◽

Signal Processor

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A Single Chip Audio Signal Processor For HDTV Receiver

1991 IEEE International Conference on Consumer Electronics ◽

10.1109/icce.1991.733168 ◽

2005 ◽

Author(s):

K. Naganawa ◽

Y. Hori ◽

S. Yanase ◽

N. Itoh ◽

Y. Asano

Keyword(s):

Audio Signal ◽

Single Chip ◽

Signal Processor

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The design of an audio signal processor system based on DSP

10.1109/icce.1988.10776 ◽

2003 ◽

Author(s):

Z. Raz

Keyword(s):

Audio Signal ◽

Signal Processor

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

Sensors ◽

10.3390/s20010172 ◽

2019 ◽

Vol 20 (1) ◽

pp. 172

Author(s):

Mariam Yiwere ◽

Eun Joo Rhee

Keyword(s):

Image Classification ◽

Sound Source ◽

Orientation Angle ◽

Audio Signal ◽

Distance Estimation ◽

Classification Problem ◽

Audio Signals ◽

Time Frequency ◽

Proposed Model ◽

Source Distance

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.

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