Abstract
Convolutional neural networks (CNN) have produced encouraging results in image classification tasks and have been increasingly adopted in audio classification applications. However, in using CNN for acoustic event recognition, the first hurdle is finding the best image representation of an audio signal. In this work, we evaluate the performance of four time-frequency representations for use with CNN. Firstly, we consider the conventional spectrogram image. Secondly, we apply moving average to the spectrogram along the frequency domain to obtain what we refer as the smoothed spectrogram. Thirdly, we use the mel-spectrogram which utilizes the mel-filter, as in mel-frequency cepstral coefficients. Finally, we propose the use of a cochleagram image the frequency components of which are based on the frequency selectivity property of the human cochlea. We test the proposed techniques on an acoustic event database containing 50 sound classes. The results show that the proposed cochleagram time-frequency image representation gives the best classification performance when used with CNN.
| Language | English |
|---|---|
| Pages | 62-66 |
| Number of pages | 5 |
| Journal | Applied Acoustics |
| Volume | 148 |
| DOIs | |
| Publication status | Published - 1 May 2019 |
| Externally published | Yes |
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Keywords
- Acoustic event recognition
- Cochleagram
- Convolutional neural network
- Mel-spectrogram
- Spectrogram
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Acoustic event recognition using cochleagram image and convolutional neural networks. / Sharan, Roneel V.; Moir, Tom J.
In: Applied Acoustics, Vol. 148, 01.05.2019, p. 62-66.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Acoustic event recognition using cochleagram image and convolutional neural networks
AU - Sharan, Roneel V.
AU - Moir, Tom J.
PY - 2019/5/1
Y1 - 2019/5/1
N2 - Convolutional neural networks (CNN) have produced encouraging results in image classification tasks and have been increasingly adopted in audio classification applications. However, in using CNN for acoustic event recognition, the first hurdle is finding the best image representation of an audio signal. In this work, we evaluate the performance of four time-frequency representations for use with CNN. Firstly, we consider the conventional spectrogram image. Secondly, we apply moving average to the spectrogram along the frequency domain to obtain what we refer as the smoothed spectrogram. Thirdly, we use the mel-spectrogram which utilizes the mel-filter, as in mel-frequency cepstral coefficients. Finally, we propose the use of a cochleagram image the frequency components of which are based on the frequency selectivity property of the human cochlea. We test the proposed techniques on an acoustic event database containing 50 sound classes. The results show that the proposed cochleagram time-frequency image representation gives the best classification performance when used with CNN.
AB - Convolutional neural networks (CNN) have produced encouraging results in image classification tasks and have been increasingly adopted in audio classification applications. However, in using CNN for acoustic event recognition, the first hurdle is finding the best image representation of an audio signal. In this work, we evaluate the performance of four time-frequency representations for use with CNN. Firstly, we consider the conventional spectrogram image. Secondly, we apply moving average to the spectrogram along the frequency domain to obtain what we refer as the smoothed spectrogram. Thirdly, we use the mel-spectrogram which utilizes the mel-filter, as in mel-frequency cepstral coefficients. Finally, we propose the use of a cochleagram image the frequency components of which are based on the frequency selectivity property of the human cochlea. We test the proposed techniques on an acoustic event database containing 50 sound classes. The results show that the proposed cochleagram time-frequency image representation gives the best classification performance when used with CNN.
KW - Acoustic event recognition
KW - Cochleagram
KW - Convolutional neural network
KW - Mel-spectrogram
KW - Spectrogram
UR - http://www.scopus.com/inward/record.url?scp=85058474671&partnerID=8YFLogxK
U2 - 10.1016/j.apacoust.2018.12.006
DO - 10.1016/j.apacoust.2018.12.006
M3 - Article
VL - 148
SP - 62
EP - 66
JO - Applied Acoustics
T2 - Applied Acoustics
JF - Applied Acoustics
SN - 0003-682X
ER -