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Research Article

Bird sounds classification by combining PNCC and robust Mel-log filter bank features

PNCC와 robust Mel-log filter bank 특징을 결합한 조류 울음소리 분류

Badi Alzahra1
,
Kyungdeuk Ko1
,
Hanseok Ko1∗
알자흐라 바디1
,
고 경득1
,
고 한석1∗
1School of Electrical Engineering, Korea University
∗Corresponding Author
31 January 2019. pp. 39-46
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Abstract

In this paper, combining features is proposed as a way to enhance the classification accuracy of sounds under noisy environments using the CNN (Convolutional Neural Network) structure. A robust log Mel-filter bank using Wiener filter and PNCCs (Power Normalized Cepstral Coefficients) are extracted to form a 2-dimensional feature that is used as input to the CNN structure. An ebird database is used to classify 43 types of bird species in their natural environment. To evaluate the performance of the combined features under noisy environments, the database is augmented with 3 types of noise under 4 different SNRs (Signal to Noise Ratios) (20 dB, 10 dB, 5 dB, 0 dB). The combined feature is compared to the log Mel-filter bank with and without incorporating the Wiener filter and the PNCCs. The combined feature is shown to outperform the other mentioned features under clean environments with a 1.34 % increase in overall average accuracy. Additionally, the accuracy under noisy environments at the 4 SNR levels is increased by 1.06 % and 0.65 % for shop and schoolyard noise backgrounds, respectively.

Keywords
Acoustic event recognition
Environmental sound classification
CNN (Convolutional Neural Network)
Weiner filter
PNCCs (Power Normalized Cepstral Coefficients)

본 논문에서는 합성곱 신경망(Convolutional Neural Network, CNN) 구조를 이용하여 잡음 환경에서 음향 신호를 분류할 때, 인식률을 높이는 결합 특징을 제안한다. 반면, Wiener filter를 이용한 강인한 log Mel-filter bank와 PNCCs(Power Normalized Cepstral Coefficients)는 CNN 구조의 입력으로 사용되는 2차원 특징을 형성하기 위해 추출됐다. 자연환경에서 43종의 조류 울음소리를 포함한 ebird 데이터베이스는 분류 실험을 위해 사용됐다. 잡음 환경에서 결합 특징의 성능을 평가하기 위해 ebird 데이터베이스를 3종류의 잡음을 이용하여 4개의 다른 SNR (Signal to Noise Ratio)(20 dB, 10 dB, 5 dB, 0 dB)로 합성했다. 결합 특징은 Wiener filter를 적용한 log-Mel filter bank, 적용하지 않은 log-Mel filter bank, 그리고 PNCC와 성능을 비교했다. 결합 특징은 잡음이 없는 환경에서 1.34 % 인식률 향상으로 다른 특징에 비해 높은 성능을 보였다. 추가적으로, 4단계 SNR의 잡음 환경에서 인식률은 shop 잡음 환경과 schoolyard 잡음 환경에서 각각 1.06 %, 0.65 % 향상했다.

키워드
음향 이벤트 인식
환경음 인식
CNN (Convolutional Neural Network)
Weiner filter
PNCCs (Power Normalized Cepstral Coefficients)
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Information
  • Publisher :The Acoustical Society of Korea
  • Publisher(Ko) :한국음향학회
  • Journal Title :The Journal of the Acoustical Society of Korea
  • Journal Title(Ko) :한국음향학회지
  • Volume : 38
  • No :1
  • Pages :39-46
  • Received Date : 2018-11-07
  • Accepted Date : 2019-01-25
  • DOI :https://doi.org/10.7776/ASK.2019.38.1.039
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