Comparison of Audio Spectral Features in a Convolutional Neural Network

Vines G, Nemer E. Comparison of Audio Spectral Features in a Convolutional Neural Network. In: AES Convention 153. Audio Engineering Society; 2022. Paper 10634. Available from: https://aes.org/publications/elibrary-page/?id=21963

@inproceedings{Vines2022_21963,
  author = {Vines, Greg and Nemer, Elias},
  title = {{Comparison of Audio Spectral Features in a Convolutional Neural Network}},
  booktitle = {AES Convention 153},
  note = {Paper 10634},
  year = {2022},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=21963}
}

TY  - CPAPER
TI  - Comparison of Audio Spectral Features in a Convolutional Neural Network
AU  - Vines, Greg
AU  - Nemer, Elias
T2  - AES Convention 153
M1  - Paper 10634
PY  - 2022
DA  - 2022/10/06
UR  - https://aes.org/publications/elibrary-page/?id=21963
PB  - Audio Engineering Society
LA  - en
AB  - Time-Frequency transformation and spectral representations of audio signals are commonly used in various machine learning applications. Typically the Mel-Spectrogram is used to create the input features to the network justified by the Mel scale’s human auditory system basis. In this paper, we compare several spectral features in a gender detection speech model comparing their performance and showing that the Mel-Spectrogram is not always the best choice for input features.
ER  -